From b667665112827047a7266fa294c884b46c7d1d7a Mon Sep 17 00:00:00 2001 From: Jie Pu Date: Thu, 7 Sep 2023 17:57:10 +0800 Subject: [PATCH 01/64] Add SIG AI networking chairs and technical leads Signed-off-by: Jie Pu Signed-off-by: chch9907 <799783944@qq.com> --- sig-ai/README.md | 20 +++++++++++++++++++- 1 file changed, 19 insertions(+), 1 deletion(-) diff --git a/sig-ai/README.md b/sig-ai/README.md index 59cbf23..3d9f331 100644 --- a/sig-ai/README.md +++ b/sig-ai/README.md @@ -11,7 +11,25 @@ The [charter](charter.md) defines the scope and governance of the AI Special Int * Regular SIG Meeting: [Thursday at 10:00 UTC+8](https://zoom.us/my/kubeedge) (weekly, starts on Nov. 12th 2020). [Convert to your timezone](https://www.thetimezoneconverter.com/?t=10%3A00%20am&tz=GMT%2B8&). * [Meeting notes and Agenda](https://docs.google.com/document/d/12n3kGUWTkAH4q2Wv5iCVGPTA_KRWav_eakbFrF9iAww/edit#). * [Meeting Calendar](https://calendar.google.com/calendar/u/0?cid=Y19nODluOXAwOG05MzFiYWM3NmZsajgwZzEwOEBncm91cC5jYWxlbmRhci5nb29nbGUuY29t) | [Subscribe](https://calendar.google.com/calendar?cid=OHJqazhvNTE2dmZ0ZTIxcWlidmxhZTNsajRAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ) - + + +### Chairs + +The Chairs of the SIG run operations and processes governing the SIG. + +- Xiaohou Shi (@xhken), China Telecom +- Zimu Zheng (@MooreZheng), Huawei + +### Technical Leads + +The Technical Leads of the SIG establish new subprojects, decommission existing subprojects, and resolve cross-subproject technical issues and decisions. + +- Jie Pu (@jaypume), Huawei +- Yutong Wang (@Ratuchetp), China Telecom + +> If any individual or organization is willing to participate in the management of our sig, please send an email to [kubeedge](kubeedge@googlegroups.com). + + ## Contact - Slack: [#sig-ai](https://kubeedge.io/docs/community/slack/) - [Mailing list](https://groups.google.com/forum/#!forum/kubeedge) From 67b467f835fe1dc103d18d82125ccb031ae32e3a Mon Sep 17 00:00:00 2001 From: wackxu Date: Tue, 28 Jun 2022 20:58:47 +0800 Subject: [PATCH 02/64] add sig testing charter Signed-off-by: wackxu Signed-off-by: chch9907 <799783944@qq.com> --- sig-testing/README.md | 26 +++++++++++++++++++++ sig-testing/charter.md | 52 ++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 78 insertions(+) create mode 100644 sig-testing/README.md create mode 100644 sig-testing/charter.md diff --git a/sig-testing/README.md b/sig-testing/README.md new file mode 100644 index 0000000..250c7ea --- /dev/null +++ b/sig-testing/README.md @@ -0,0 +1,26 @@ +# Testing Special Interest Group + +Interested in how we can most effectively test KubeEdge. We're interested specifically in making it easier for the community to run tests and contribute test results, to ensure KubeEdge is stable across a variety of cluster configurations and cloud providers. + +The [charter](https://github.com/kubeedge/community/blob/master/sig-testing/charter.md) defines the scope and governance of the Testing Special Interest Group. + +- ## Meetings + + - Regular SIG Meeting: [Monday at 15:30 UTC+8](https://zoom.us/j/4167237304) (biweekly, starts on Jun. 28th 2022). [Convert to your timezone](https://www.thetimezoneconverter.com/?t=10%3A00 am&tz=GMT%2B8&). + - [Meeting notes and Agenda](https://docs.google.com/document/d/1PeuZzfLjU8oIduKI2nFeckKAhhckME4tqCZ33WmORBI/edit#heading=h.252sw1n3b1vb). + - [Meeting Calendar](https://calendar.google.com/calendar/embed?src=8rjk8o516vfte21qibvlae3lj4%40group.calendar.google.com) | [Subscribe](https://calendar.google.com/calendar?cid=OHJqazhvNTE2dmZ0ZTIxcWlidmxhZTNsajRAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ) + + ## Contact + + - [#sig-testing slack channel](https://kubeedge.slack.com) + - [Open Community Issues](https://github.com/kubeedge/community/issues) + - [Mailing list](https://groups.google.com/forum/#!forum/kubeedge) + + ## Leadership + + ### Chairs + + The Chairs of the SIG run operations and processes governing the SIG. + + - XsWack (**[@wackxu](https://github.com/wackxu)**) + diff --git a/sig-testing/charter.md b/sig-testing/charter.md new file mode 100644 index 0000000..6d6cf58 --- /dev/null +++ b/sig-testing/charter.md @@ -0,0 +1,52 @@ +# SIG Testing Charter + +This charter adheres to the conventions described in [KubeEdge Open Governance](https://github.com/kubeedge/community/blob/master/GOVERNANCE.md) and uses the Roles and Organization Management outlined in the governance doc. + +## Scope + +SIG Testing is interested in effective testing of KubeEdge and automating away project toil. We focus on creating and running tools and infrastructure that make it easier for the community to write and run tests, and to contribute, analyze and act upon test results. + +### In scope + +- Project CI and workflow automation via tools + + - Configuration management of CI jobs + + - Metrics, reports, and dashboards for tests that execute in CI jobs + - Test-specific frameworks, tools + +- Infrastructure to support running project CI at scale + +- Tools, frameworks and libraries that make it possible to write tests against KubeEdge such as e2e + +- Enable other SIGs to efficiently protect their own code from defects + - Pull Request reviews from other SIGs + - SIG-Testing may be added to any pull request to request testing advice or review + - Track code test coverage and drive other SIGs continuous improvement + - Audit existing code for efficiency and correctness + - Identify inefficient tests and help them execute faster + - Set consistent quality standards between SIGs + - Raise this standard over time + + +### Out of scope +- We are not responsible for writing, fixing nor actively troubleshooting tests for features or subprojects owned by other SIGs +- Maintain all individual tests on behalf of other SIGs + - Write new automated tests for each feature + - Fix individual broken tests or the product bugs they point to + - Approve every pull request which targets the main development branch + - Track which tests are not automated/automatable in a feature area + - Monitoring individual test health or their metrics + - Defining software performance requirements + +## Roles and Organization Management + +This SIG follows and adheres to the Roles and Organization Management outlined in KubeEdge Open Governance and opts-in to updates and modifications to KubeEdge Open Governance. + +### Additional responsibilities of Chairs + +- Manage and curate the project boards associated with all sub-projects ahead of every SIG meeting so they may be discussed +- Ensure the agenda is populated 24 hours in advance of the meeting, or the meeting is then cancelled +- Report the SIG status at events and community meetings wherever possible +- Actively promote diversity and inclusion in the SIG +- Uphold the KubeEdge Code of Conduct especially in terms of personal behavior and responsibility From 2b981a9b7dc8835171650aa1514c99dabfa5464a Mon Sep 17 00:00:00 2001 From: Shelley-BaoYue Date: Mon, 4 Sep 2023 19:40:43 +0800 Subject: [PATCH 03/64] modify README of sig testing Signed-off-by: Shelley-BaoYue Signed-off-by: chch9907 <799783944@qq.com> --- sig-testing/README.md | 28 ++++++++++++++++------------ 1 file changed, 16 insertions(+), 12 deletions(-) diff --git a/sig-testing/README.md b/sig-testing/README.md index 250c7ea..c6b8dfe 100644 --- a/sig-testing/README.md +++ b/sig-testing/README.md @@ -4,23 +4,27 @@ Interested in how we can most effectively test KubeEdge. We're interested specif The [charter](https://github.com/kubeedge/community/blob/master/sig-testing/charter.md) defines the scope and governance of the Testing Special Interest Group. -- ## Meetings +## Meetings - - Regular SIG Meeting: [Monday at 15:30 UTC+8](https://zoom.us/j/4167237304) (biweekly, starts on Jun. 28th 2022). [Convert to your timezone](https://www.thetimezoneconverter.com/?t=10%3A00 am&tz=GMT%2B8&). - - [Meeting notes and Agenda](https://docs.google.com/document/d/1PeuZzfLjU8oIduKI2nFeckKAhhckME4tqCZ33WmORBI/edit#heading=h.252sw1n3b1vb). - - [Meeting Calendar](https://calendar.google.com/calendar/embed?src=8rjk8o516vfte21qibvlae3lj4%40group.calendar.google.com) | [Subscribe](https://calendar.google.com/calendar?cid=OHJqazhvNTE2dmZ0ZTIxcWlidmxhZTNsajRAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ) +*TBD* - ## Contact +## Contact - - [#sig-testing slack channel](https://kubeedge.slack.com) - - [Open Community Issues](https://github.com/kubeedge/community/issues) - - [Mailing list](https://groups.google.com/forum/#!forum/kubeedge) +- [#sig-testing slack channel](https://kubeedge.slack.com) +- [Open Community Issues](https://github.com/kubeedge/community/issues) +- [Mailing list](https://groups.google.com/forum/#!forum/kubeedge) - ## Leadership +## Leadership - ### Chairs +### Chairs - The Chairs of the SIG run operations and processes governing the SIG. +The Chairs of the SIG run operations and processes governing the SIG. - - XsWack (**[@wackxu](https://github.com/wackxu)**) +*TODO* + +### Technical Leads + +The Technical Leads of the SIG establish new subprojects, decommission existing subprojects, and resolve cross-subproject technical issues and decisions. + +- Shelley Bao (@Shelley-BaoYue), Huawei From d4ba67eeb45ca24f9bbee4e9d8b756bc24f52bc5 Mon Sep 17 00:00:00 2001 From: zhiying Date: Thu, 14 Mar 2024 17:37:45 +0800 Subject: [PATCH 04/64] update 2024 roadmap Signed-off-by: zhiying Signed-off-by: chch9907 <799783944@qq.com> --- roadmap.md | 189 ++++++++++++++++++----------------------------------- 1 file changed, 65 insertions(+), 124 deletions(-) diff --git a/roadmap.md b/roadmap.md index add2250..54198b1 100644 --- a/roadmap.md +++ b/roadmap.md @@ -4,154 +4,95 @@ This document defines a high level roadmap for KubeEdge development. The [milestones defined in GitHub](https://github.com/kubeedge/kubeedge/milestones) represent the most up-to-date plans. -The roadmap below outlines KubeEdge’s 2023 feature plan. +The roadmap below outlines KubeEdge’s 2024 feature plan. -## 2023 H1 +## SIG Node -### SIG Node +- Continuous follow up Kubernetes release +- Support edge nodes running on mac OS and RTOS +- Support in-cluster config for edge pods to access Kube-APIServer +- Enhancements to device plugin on edge nodes, such as support for multiple virtual GPUs +- Support event report to cloud +- Support for serverless computing +- Remote maintenance +- Add edge nodes in batches -- Support WasmEdge integration on KubeEdge edgenode -- Support Kubectl attach to container running on edgenode -- Update Kubernetes dependency to v1.24.14, switch default container runtime at edge to containerd +## SIG Device-IOT -### SIG Device-IOT +- Multi-language Mappers support +- Devices discovery +- Integration with time-series databases and other databases +- Video stream +- Enhance device management capabilities, such as device writing and device status monitoring -- Provide Modbus mapper based on DMI -- DMI Data plane -- Mapper framework support for DMI +## SIG Security -### SIG Security +- SLSA / CodeQL (There is still some provenance work remaining to reach SLSA L4) +- Spiffe research +- Support for certificates with multiple encryption algorithms, and provide interface capabilities +- Add admission for edge-cloud Messaging Channel -- Support authentication and authorization for Kube-API endpoint for applications on edge nodes -- Enhancements for edge plaintext storage, ensure tokens are not persisted on disk +## SIG Scalability -### SIG Scalability +- Scalability and performance testing with EdgeMesh integrated +- Scalability and performance testing for IoT devices scenario -- Support cluster scope resource reliable delivery to edge nodes -- CloudCore memory usage is reduced by 40%, through unified generic informer and reduce unnecessary cache +## Stability -### SIG Networking +- Stability maintenance of CloudCore, including stability testing and issue resolution +- EdgeMesh stability +- Enhanced reliability of cloud-edge collaboration, such as stability improvement of - Edge Kube-API interface and logs/exec feature -- Add configurable field TunnelLimitConfig to edge-tunnel module -- EdgeMesh container network supports CNI features +## SIG Networking -### SIG AI - -- Sedna - - Support unstructured lifelong learning - - Support unseen task recognition - - Support displaying knowledge base - -- Ianvs - - Support lifelong learning throughout entire lifecycle - - Provide classic lifelong learning testing metrics and support for visualizing test results - - Provide real-world datasets and rich examples for lifelong learning testing - -### SIG Testing - -- Provide node conformance test suite -- Improve unit test coverage - -### SIG Cluster-Lifecycle - -- Provide a tool keink for running local KubeEdge clusters using Docker container “nodes” - -### UI - -- Alpha version of KubeEdge Dashboard -- Re-design KubeEdge website - -## 2023 H2 - -### SIG Node - -- Support edge nodes running on Windows. -- Capabilities enhancements for edge nodes, such as support static pods, event reporting and configurable application migration policies. -- Support edge nodes running on RTOS systems. -- Enhancements to device plugin on edge node, such as support for multiple virtual GPUs. -- Support for serverless computing. -- Feature of upgrading edge nodes from cloud move to GA. -- Optimization of node group features, such as support for more differentiated configuration parameters. - -### SIG Device-IOT - -- DMI data plane support (H1 has completed design). -- Migration solution for edge devices among multi-nodes based on DMI. -- Mapper framework support for DMI. -- Research on multi-language mappers support. -- Integration with time-series databases and other databases. -- Refactoring of Device and DeviceModel CRDs. -- Enhanced reliability for custom message transmission. - -### SIG Security - -- SLSA / CodeQL (There is still some provenance work remaining to reach SLSA L4). -- Spiffe Research. -- Support for certificates with multiple encryption algorithms, and provide interface capabilities. +- ServichMesh + - Combined with projects such as istio or kmesh to bring richer service mesh functions to edge scenarios. +- Large-scale optimization + - In large-scale deployments, there is a high load on the edge kube-apiserver. Consider using IPVS (IP Virtual Server) technology to handle the requests efficiently + - Having a large number of services significantly increases the number of iptables rules on the nodes +- Performance optimization: Kernel-level traffic forwarding based on eBPF (extended Berkeley Packet Filter) +- Distributed messaging system -### SIG Scalability +## SIG AI -- Scalability and performance testing with EdgeMesh integrated. -- Scalability and performance testing for IoT devices scenario. +- Distributed deployment of the LLM model + - Deploy a large language model (LLM) on multiple edge nodes using KubeEdge. The LLM can be used for various natural language processing tasks, such as code implementations, text generation, machine translation, summarization, etc + - The distributed deployment can reduce the computation consumption of the LLM, as well as improve its scalability and fault tolerance +- Edge-Cloud benchmarking of the LLM model + - Compare the performance and resource consumption of the LLM model running on the edge nodes versus the cloud servers using KubeEdge. The LLM can be evaluated on different metrics, such as accuracy, speed, memory, CPU, etc + - The benchmarking can help optimize the LLM model for different scenarios and environments, as well as identify the trade-offs and challenges of edge-cloud collaboration +- Integration of different types of LLM models + - Integrate different types of LLM models, such as large language/ visual/ multi-modal models, with KubeEdge. The LLM models can be combined to achieve more complex and diverse language generation and understanding tasks, such as question answering, dialogue, image captioning, etc + - The integration can leverage the advantages of each LLM model and enhance the overall functionality and capability of the edge-cloud system -### Stability +## SIG Robotics -- Stability maintenance of CloudCore, including stability testing and issue resolution. -- EdgeMesh stability. -- Enhanced reliability of cloud-edge collaboration, such as stability improvement of Edge Kube-API interface and logs/exec feature. +- Universal robot control system + - The standard protocol for robot control systems has been open sourced(https://github.com/kubeedge/robolink), and a universal robot control system will be implemented based on this standard in the future -### SIG Testing +## SIG Testing - Increase unit test coverage Improve. -- Improve e2e test case coverage (scenario-based coverage). +- Improve e2e test case coverage - Integration testing. -- Runtime and K8s version compatibility test. -- Keadm cross version compatibility test. -- Cloud-Edge cross version compatibility test. - -### SIG Networking - -- Node offline optimization - - When a node goes offline, other nodes receive the update and remove the corresponding backend from the endpoint. -- Large-scale optimization - - In large-scale deployments, there is a high load on the edge kube apiserver. Consider using IPVS (IP Virtual Server) technology to handle the requests efficiently. - - Having a large number of services significantly increases the number of iptables rules on the nodes. - - Container Network supports CNI features. -- Performance optimization: Kernel-level traffic forwarding based on eBPF (extended Berkeley Packet Filter). -- Distributed messaging system. - -### SIG Cluster-Lifecycle - -- Support for Windows installation and deployment. -- Pre-download of images for edge applications. -- Router High Availability (HA) support. - -### Docs - -- Optimization of website documentation, including directory restructuring and improved comprehensiveness. -- Support for updating documentation versions. -- Completion of official website documentation, including the DMI developer guide, operational guide for monitoring and etc. -- Publish cases on website case studies. - -### UI - -- Dashboard release iteration. -- Add case studies on website. -- Add job center on website. -- Support for versioning on the website. +- Conformance test improve +- Perform testing on the hardware requisites required for KubeEdge, such as memory usage, bandwidth, and other metrics -### SIG AI +## SIG Cluster-Lifecycle -- Support semi-automatic annotation in edge-cloud collaborative lifelong learning. -- Support runtime unseen task processing in edge-cloud collaborative lifelong learning. -- Support advanced offline unseen task processing in edge-cloud collaborative lifelong learning. +- Router High Availability (HA) support +- Enhancement for Keink tool, Keadm tool +- Edgecore config can be used for Keadm join +- Enhance the installation tool Keadm +- Optimize installation(keadm join) process +- Enhancement for image prepull +- Support OTA mode -### SIG Robotics +## UI -- Add RoboDev Repository: Make it easier for developers to build robotic applications. -- Add RTF(ready to fly) Robotics E2E solutions: Teleoperation, RoboPilot. +- Dashboard release iteration -### Experience +## Experience - Example library enhancement -- Go online to Killer-Coda +- Go online to Killer-Coda \ No newline at end of file From 5dcf7f0a380655e6d60ea2a889171c83979869c0 Mon Sep 17 00:00:00 2001 From: Kevin Wang Date: Tue, 7 May 2024 12:21:49 +0800 Subject: [PATCH 05/64] fix typo of TSC dir Signed-off-by: Kevin Wang Signed-off-by: chch9907 <799783944@qq.com> --- GOVERNANCE.md | 3 ++- OWNERS | 4 ++-- OWNERS_ALIASES | 2 +- .../README.md | 0 .../charter.md | 0 5 files changed, 5 insertions(+), 4 deletions(-) rename {commitee-technical-steering => committee-technical-steering}/README.md (100%) rename {commitee-technical-steering => committee-technical-steering}/charter.md (100%) diff --git a/GOVERNANCE.md b/GOVERNANCE.md index d4813cc..7059778 100644 --- a/GOVERNANCE.md +++ b/GOVERNANCE.md @@ -192,4 +192,5 @@ Sections of this documents have been borrowed from [Kubernetes governance](https [community members]: /community-membership.md -[tsc charter]: /commitee-technical-steering/charter.md \ No newline at end of file +[tsc charter]: /committee-technical-steering/charter.md + diff --git a/OWNERS b/OWNERS index 8c4ad47..979c75d 100644 --- a/OWNERS +++ b/OWNERS @@ -1,5 +1,5 @@ approvers: - - commitee-technical-steering + - committee-technical-steering reviewers: - - commitee-technical-steering + - committee-technical-steering diff --git a/OWNERS_ALIASES b/OWNERS_ALIASES index e3b7315..3d4a140 100644 --- a/OWNERS_ALIASES +++ b/OWNERS_ALIASES @@ -1,5 +1,5 @@ aliases: - commitee-technical-steering: + committee-technical-steering: - benjaminhuo - dingyin - fisherxu diff --git a/commitee-technical-steering/README.md b/committee-technical-steering/README.md similarity index 100% rename from commitee-technical-steering/README.md rename to committee-technical-steering/README.md diff --git a/commitee-technical-steering/charter.md b/committee-technical-steering/charter.md similarity index 100% rename from commitee-technical-steering/charter.md rename to committee-technical-steering/charter.md From d76924fac5e4586dcb37291b123a66b89d81b75c Mon Sep 17 00:00:00 2001 From: Shelley-BaoYue Date: Tue, 7 Mar 2023 16:52:59 +0800 Subject: [PATCH 06/64] support wasmedge Signed-off-by: Shelley-BaoYue Signed-off-by: chch9907 <799783944@qq.com> --- sig-node/wasmedge/support_wasmedge.md | 192 +++++++++++++++++++++++ sig-node/wasmedge/support_wasmedge_cn.md | 189 ++++++++++++++++++++++ 2 files changed, 381 insertions(+) create mode 100644 sig-node/wasmedge/support_wasmedge.md create mode 100644 sig-node/wasmedge/support_wasmedge_cn.md diff --git a/sig-node/wasmedge/support_wasmedge.md b/sig-node/wasmedge/support_wasmedge.md new file mode 100644 index 0000000..35fe35f --- /dev/null +++ b/sig-node/wasmedge/support_wasmedge.md @@ -0,0 +1,192 @@ +WasmEdge is a lightweight, high-performance, and extensible WebAssembly runtime for cloud native. It is compliant with OCI standard and currently is a CNCF (Cloud Native Computing Foundation) Sandbox project. It powers edge computing, serverless apps, microservices, service mesh, and IoT devices. + +In this article, we will introduce how to run a WasmEdge simple demo app with containerd over KubeEdge. [KubeEdge+WasmEdge+CRI-O](https://wasmedge.org/book/en/use_cases/kubernetes/kubernetes/kubeedge.html) also provides the operation steps about running a WasmEdge demo app with CRI-O over KubeEdge. + +We assume that you have completed the installation of the Kubernetes cluster. [Creating a cluster with kubeadm](https://kubernetes.io/docs/setup/production-environment/tools/kubeadm/create-cluster-kubeadm/) will help you create cluster with kubeadm. + +# Install Containerd And WasmEdge on Edge Node + +WasmEdge provides the one-click installation scripts. You can directly execute: + +``` +wget -qO- https://raw.githubusercontent.com/second-state/wasmedge-containers-examples/main/containerd/install.sh | bash +```` + +to complete the installation of containerd、WasmEdge and crun. See [installation scripts](https://github.com/second-state/wasmedge-containers-examples/blob/main/containerd/install.sh) to learn more details. + +Next we will introduce the process step by step. + +## 1. Install containerd + +* See [quick start with containerd](https://github.com/containerd/containerd/blob/main/docs/getting-started.md), download the official binary and install it. + +* Configure containerd: + + ``` + sudo mkdir -p /etc/containerd + sudo bash -c "containerd config default > /etc/containerd/config.toml" + ``` + +## 2. Install WasmEdge + +Use the [install script](https://github.com/WasmEdge/WasmEdge/blob/master/utils/install.sh) to install WasmEdge on your edge node. + +``` +curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | bash +``` + +## 3. Install crun and modify containerd config + +The crun project has WasmEdge support baked in. Next we need to get the crun binary and install it. + +* Configure the Dependencies Required + +``` +$ sudo apt update +$ sudo apt install -y make git gcc build-essential pkgconf libtool \ + libsystemd-dev libprotobuf-c-dev libcap-dev libseccomp-dev libyajl-dev \ + go-md2man libtool autoconf python3 automake +``` + +* Configure, build, and install a crun binary with WasmEdge support. + +``` +git clone https://github.com/containers/crun +cd crun +./autogen.sh +./configure --with-wasmedge +make +sudo make install +``` + +* Modify containerd config and start containerd. + +It mainly includes two modifications: +(1) Configure containerd to use `crun` as the low-level OCI runtime; +(2) Set pod_annotations: `pod_annotations = ["module.wasm.image/ variable .*"]`. + +``` +wget https://raw.githubusercontent.com/second-state/wasmedge-containers-examples/main/containerd/containerd_config.diff +sudo patch -d/ -p0 < containerd_config.diff +sudo systemctl start containerd +``` + +# Install KubeEdge + +After completing the above work, you can install KubeEdge according to the official website [Installation Guide](https://kubeedge.io/en/docs/setup/keadm/), which mainly includes three steps. + +## 1. Install Keadm + +``` +wget https://github.com/kubeedge/kubeedge/releases/download/v1.12.1/keadm-v1.12.1-linux-amd64.tar.gz +tar -zxvf keadm-v1.12.1-linux-amd64.tar.gz +cp keadm-v1.12.1-linux-amd64/keadm/keadm /usr/local/bin/keadm +``` + +## 2. Setup cloud side (CloudCore) + +``` +keadm init --advertise-address="THE-EXPOSED-IP" --profile version=v1.13.0 --kube-config=/root/.kube/config +``` + +After installing CloudCore, run `keadm gettoken` in cloud side will return the token, which will be used when joining edge nodes. + +## 3. Install EdgeCore and join edge node + +KubeEdge v1.13.0 uses containerd as the default container runtime, you don't need to specify the runtime type when installing EdgeCore. If you want to install KubeEdge with version earlier than v1.13.0, `--runtimetype` and `--remote-runtime-endpoint` need be used to specify the container runtime when executing `keadm join`. + +``` +keadm join --cloudcore-ipport="THE-EXPOSED-IP":10000 --token=$token} --kubeedge-version=v1.13.0 +``` + +# Enable Kubelet Logs Feature + +Since the WebAssembly application used in the demo needs to enable Kubelet logs feature, please see [Enable Kubectl logs Feature](https://kubeedge.io/en/docs/setup/keadm/#enable-kubectl-logs-feature) to turn on it. + +# Run a Simple WebAssembly App + +We can run the WebAssembly-based image in the Kubernetes cluster. + +``` +$ kubectl run -it --restart=Never wasi-demo --image=hydai/wasm-wasi-example:with-wasm-annotation --annotations="module.wasm.image/variant=compat-smart" /wasi_example_main.wasm 50000000 + +Random number: 626588879 +Random bytes: [175, 254, 19, 202, 67, 26, 244, 82, 225, 201, 104, 99, 152, 44, 222, 233, 182, 185, 95, 166, 130, 74, 36, 88, 88, 69, 141, 106, 155, 79, 80, 7, 91, 239, 112, 27, 182, 103, 49, 215, 171, 109, 80, 51, 190, 237, 166, 167, 87, 10, 235, 81, 159, 75, 22, 161, 94, 12, 97, 157, 216, 223, 41, 80, 5, 137, 124, 89, 158, 246, 1, 109, 20, 90, 125, 29, 236, 239, 238, 7, 195, 1, 244, 241, 226, 145, 118, 44, 235, 250, 225, 155, 210, 235, 137, 9, 194, 118, 72, 251, 113, 255, 164, 110, 94, 212, 150, 59, 228, 220, 164, 243, 68, 64, 77, 115, 124, 70, 201, 111, 73, 171, 27, 0, 225, 130, 80, 66] +Printed from wasi: This is from a main function +This is from a main function +The env vars are as follows. +PATH: /usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin +TERM: xterm +HOSTNAME: wasi-demo +HOME: / +The args are as follows. +/wasi_example_main.wasm +50000000 +File content is This is in a file +``` + +According to the pod log, the WebAssembly app of pod successfully deploy to edge node. + +``` +$ kubectl describe pod wasi-demo + +Name: wasi-demo +Namespace: default +Priority: 0 +Node: kubeedge-dev-linux-baoyue/192.168.1.123 +Start Time: Mon, 06 Mar 2023 19:41:42 +0800 +Labels: run=wasi-demo +Annotations: module.wasm.image/variant: compat-smart +Status: Succeeded +IP: 10.88.0.13 +IPs: + IP: 10.88.0.13 + IP: 2001:4860:4860::d +Containers: + wasi-demo: + Container ID: containerd://1e1df3ece5d3d67ead3375e82df039d617b9698e421be2a085eadbfe273b2a06 + Image: hydai/wasm-wasi-example:with-wasm-annotation + Image ID: docker.io/hydai/wasm-wasi-example@sha256:525aab8d6ae8a317fd3e83cdac14b7883b92321c7bec72a545edf276bb2100d6 + Port: + Host Port: + Args: + /wasi_example_main.wasm + 50000000 + State: Terminated + Reason: Completed + Exit Code: 0 + Started: Mon, 06 Mar 2023 19:41:50 +0800 + Finished: Mon, 06 Mar 2023 19:41:50 +0800 + Ready: False + Restart Count: 0 + Environment: + Mounts: + /var/run/secrets/kubernetes.io/serviceaccount from kube-api-access-lznkt (ro) +Conditions: + Type Status + Initialized True + Ready False + ContainersReady False + PodScheduled True +Volumes: + kube-api-access-lznkt: + Type: Projected (a volume that contains injected data from multiple sources) + TokenExpirationSeconds: 3607 + ConfigMapName: kube-root-ca.crt + ConfigMapOptional: + DownwardAPI: true +QoS Class: BestEffort +Node-Selectors: +Tolerations: node.kubernetes.io/not-ready:NoExecute op=Exists for 300s + node.kubernetes.io/unreachable:NoExecute op=Exists for 300s +Events: +``` + +It can also be seen on the edge node that the container successfully completed and exited + +``` +crictl ps -a + +CONTAINER IMAGE CREATED STATE NAME ATTEMPT POD ID +83aafc8745f86 0423b8eb71e31 7 seconds ago Exited wasi-demo 0 b23754a9ee905 +``` \ No newline at end of file diff --git a/sig-node/wasmedge/support_wasmedge_cn.md b/sig-node/wasmedge/support_wasmedge_cn.md new file mode 100644 index 0000000..7c3eb4b --- /dev/null +++ b/sig-node/wasmedge/support_wasmedge_cn.md @@ -0,0 +1,189 @@ +WasmEdge是一个由CNCF托管、符合OCI标准的云原生WebAssembly运行时,拥有高性能、更轻量、更安全等优势,目前广泛应用于边缘计算、Serverless应用、微服务、服务网格和IoT设备等技术。 + +本篇文章将介绍如何在KubeEdge的边缘节点中,使用containerd运行一个WasmEdge的demo。[KubeEdge+WasmEdge+CRI-O](https://wasmedge.org/book/zh/kubernetes/kubernetes/kubeedge.html)同样提供了使用CRI-O运行WasmEdge的操作步骤。 + +本文假设您已经完成K8s集群的安装,可参阅[使用kubeadm创建集群](https://kubernetes.io/zh-cn/docs/setup/production-environment/tools/kubeadm/create-cluster-kubeadm/)。 + +# 在边缘节点上安装containerd和WasmEdge + +WasmEdge已经提供了一键安装脚本,直接执行: +``` +wget -qO- https://raw.githubusercontent.com/second-state/wasmedge-containers-examples/main/containerd/install.sh | bash +```` +即可完成containerd、WasmEdge和crun的一键安装,可以参阅[安装脚本](https://github.com/second-state/wasmedge-containers-examples/blob/main/containerd/install.sh)。 + +本文会逐步介绍安装过程。 + +## 1. 安装containerd + +* 参阅[开始使用containerd](https://github.com/containerd/containerd/blob/main/docs/getting-started.md),下载官方二进制文件,并执行安装 + +* 配置containerd: + + ``` + sudo mkdir -p /etc/containerd + sudo bash -c "containerd config default > /etc/containerd/config.toml" + ``` + +## 2. 安装WasmEdge + +使用WasmEdge提供的[安装脚本](https://github.com/WasmEdge/WasmEdge/blob/master/utils/install.sh)安装WasmEdge。 + +``` +curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | bash +``` + +## 3. 安装crun并修改containerd配置 + +crun项目已经内置了对WasmEdge的支持,接下来我们需要获取一个支持WasmEdge的crun二进制文件,并完成安装与配置。 + +* 配置crun需要的依赖 + +``` +$ sudo apt update +$ sudo apt install -y make git gcc build-essential pkgconf libtool \ + libsystemd-dev libprotobuf-c-dev libcap-dev libseccomp-dev libyajl-dev \ + go-md2man libtool autoconf python3 automake +``` + +* 配置、构建并安装一个支持WasmEdge的crun + +``` +git clone https://github.com/containers/crun +cd crun +./autogen.sh +./configure --with-wasmedge +make +sudo make install +``` + +* 修改containerd的配置,并启动containerd服务。 + +主要包括两处修改: +(1)将containerd配置为使用`crun`作为底层OCI runtime; +(2)设置pod_annotations来传播到OCI规范:`pod_annotations = ["module.wasm.image/ variable .*"]`。 + +``` +wget https://raw.githubusercontent.com/second-state/wasmedge-containers-examples/main/containerd/containerd_config.diff +sudo patch -d/ -p0 < containerd_config.diff +sudo systemctl start containerd +``` + +# 安装kubeedge + +在完成上述工作之后,即可按照KubeEdge官网[安装指南](https://kubeedge.io/en/docs/setup/keadm/)开始安装,主要包括三个步骤。 + +## 1. 安装Keadm + +``` +wget https://github.com/kubeedge/kubeedge/releases/download/v1.12.1/keadm-v1.12.1-linux-amd64.tar.gz +tar -zxvf keadm-v1.12.1-linux-amd64.tar.gz +cp keadm-v1.12.1-linux-amd64/keadm/keadm /usr/local/bin/keadm +``` + +## 2. 在云端节点安装CloudCore + +``` +keadm init --advertise-address="THE-EXPOSED-IP" --profile version=v1.13.0 --kube-config=/root/.kube/config +``` + +CloudCore安装完成后,使用`keadm gettoken`获取token,用于安装EdgeCore时使用。 + +## 3. 在边缘节点安装EdgeCore + +由于KubeEdge v1.13.0默认使用容器运行时为containerd,因此在安装EdgeCore时可以不指定容器运行时。如果安装的是v1.13.0之前的版本,需要在执行keadm join命令时,使用`--runtimetype`和`--remote-runtime-endpoint`指定边缘节点需要的容器运行时。 + +``` +keadm join --cloudcore-ipport="THE-EXPOSED-IP":10000 --token=$token} --kubeedge-version=v1.13.0 +``` + +# 启用Kubectl日志功能 + +由于本次演示所使用的WebAssembly应用需要启动Kubectl logs功能,因此需要打开KubeEdge的Logs/Exec开关,具体步骤参考[Enable Kubectl logs Feature](https://kubeedge.io/zh/docs/setup/keadm/#enable-kubectl-logs-feature) + +# 运行一个简单的WebAssembly应用 + +我们可以直接在云端运行基于WebAssembly的镜像。 + +``` +$ kubectl run -it --restart=Never wasi-demo --image=hydai/wasm-wasi-example:with-wasm-annotation --annotations="module.wasm.image/variant=compat-smart" /wasi_example_main.wasm 50000000 + +Random number: 626588879 +Random bytes: [175, 254, 19, 202, 67, 26, 244, 82, 225, 201, 104, 99, 152, 44, 222, 233, 182, 185, 95, 166, 130, 74, 36, 88, 88, 69, 141, 106, 155, 79, 80, 7, 91, 239, 112, 27, 182, 103, 49, 215, 171, 109, 80, 51, 190, 237, 166, 167, 87, 10, 235, 81, 159, 75, 22, 161, 94, 12, 97, 157, 216, 223, 41, 80, 5, 137, 124, 89, 158, 246, 1, 109, 20, 90, 125, 29, 236, 239, 238, 7, 195, 1, 244, 241, 226, 145, 118, 44, 235, 250, 225, 155, 210, 235, 137, 9, 194, 118, 72, 251, 113, 255, 164, 110, 94, 212, 150, 59, 228, 220, 164, 243, 68, 64, 77, 115, 124, 70, 201, 111, 73, 171, 27, 0, 225, 130, 80, 66] +Printed from wasi: This is from a main function +This is from a main function +The env vars are as follows. +PATH: /usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin +TERM: xterm +HOSTNAME: wasi-demo +HOME: / +The args are as follows. +/wasi_example_main.wasm +50000000 +File content is This is in a file +``` + +由上述pod日志可以看出WebAssembly应用的pod成功部署到边缘节点,容器成功执行完成并退出。 +``` +$ kubectl describe pod wasi-demo + +Name: wasi-demo +Namespace: default +Priority: 0 +Node: kubeedge-dev-linux-baoyue/192.168.1.123 +Start Time: Mon, 06 Mar 2023 19:41:42 +0800 +Labels: run=wasi-demo +Annotations: module.wasm.image/variant: compat-smart +Status: Succeeded +IP: 10.88.0.13 +IPs: + IP: 10.88.0.13 + IP: 2001:4860:4860::d +Containers: + wasi-demo: + Container ID: containerd://1e1df3ece5d3d67ead3375e82df039d617b9698e421be2a085eadbfe273b2a06 + Image: hydai/wasm-wasi-example:with-wasm-annotation + Image ID: docker.io/hydai/wasm-wasi-example@sha256:525aab8d6ae8a317fd3e83cdac14b7883b92321c7bec72a545edf276bb2100d6 + Port: + Host Port: + Args: + /wasi_example_main.wasm + 50000000 + State: Terminated + Reason: Completed + Exit Code: 0 + Started: Mon, 06 Mar 2023 19:41:50 +0800 + Finished: Mon, 06 Mar 2023 19:41:50 +0800 + Ready: False + Restart Count: 0 + Environment: + Mounts: + /var/run/secrets/kubernetes.io/serviceaccount from kube-api-access-lznkt (ro) +Conditions: + Type Status + Initialized True + Ready False + ContainersReady False + PodScheduled True +Volumes: + kube-api-access-lznkt: + Type: Projected (a volume that contains injected data from multiple sources) + TokenExpirationSeconds: 3607 + ConfigMapName: kube-root-ca.crt + ConfigMapOptional: + DownwardAPI: true +QoS Class: BestEffort +Node-Selectors: +Tolerations: node.kubernetes.io/not-ready:NoExecute op=Exists for 300s + node.kubernetes.io/unreachable:NoExecute op=Exists for 300s +Events: +``` + +在边缘节点也可以查看对应的容器状态。 + +``` +crictl ps -a + +CONTAINER IMAGE CREATED STATE NAME ATTEMPT POD ID +83aafc8745f86 0423b8eb71e31 7 seconds ago Exited wasi-demo 0 b23754a9ee905 +``` \ No newline at end of file From d9fe7c581dd3cecdca1425b416f4b8f21785578b Mon Sep 17 00:00:00 2001 From: zhiying Date: Wed, 15 May 2024 15:20:17 +0800 Subject: [PATCH 07/64] update 2024 roadmap_zh Signed-off-by: zhiying Signed-off-by: chch9907 <799783944@qq.com> --- roadmap_zh.md | 165 ++++++++++++++++---------------------------------- 1 file changed, 53 insertions(+), 112 deletions(-) diff --git a/roadmap_zh.md b/roadmap_zh.md index bfa84a0..d0bec89 100644 --- a/roadmap_zh.md +++ b/roadmap_zh.md @@ -4,154 +4,95 @@ [在GitHub中定义的里程碑](https://github.com/kubeedge/kubeedge/milestones)代表了最新的计划。 -下面的路线图概述了KubeEdge 2023年度功能特性规划。 +下面的路线图概述了KubeEdge 2024年度功能特性规划。 -## 2023 H1 +## SIG Node -### SIG Node - -- 支持在边缘节点使用WasmEdge -- 支持使用Kubectl attach命令访问边缘容器 -- 升级Kubernetes依赖版本到v1.24.14, 将边缘默认容器运行时切换成containerd - -### SIG Device-IOT - -- 提供基于DMI的modbus mapper -- DMI数据面 -- 提供支持DMI的mapper框架 - -### SIG Security - -- 为边缘节点上的应用程序提供支持Kube-API端点的身份验证和授权 -- 增强边缘明文存储,确保token不落盘 - -### SIG Scalability - -- 支持集群scope资源可靠地分发到边缘节点 -- 通过统一通用informer和减少不必要的缓存,将CloudCore内存占用降低了40% - -### SIG Networking - -- 为edge-tunnel模块新增可配置的TunnelLimitConfig字段 -- EdgeMesh容器网络支持CNI特性 - -### SIG AI - -- Sedna - - 支持非结构化的终身学习 - - 支持未知任务识别 - - 支持展示知识库 - -- Ianvs - - 支持整个生命周期的终身学习 - - 提供经典的终身学习测试指标和支持可视化测试结果 - - 提供真实世界的数据集和丰富的终身学习测试示例 - -### SIG Testing - -- 提供硬件兼容性测试套件Provide node conformance test suite -- 提高单元测试覆盖率Improve unit test coverage - -### SIG Cluster-Lifecycle - -- 提供工具keink,用于使用Docker容器运行本地KubeEdge集群 - -### UI - -- 提供KubeEdge Dashboard的Alpha版本 -- 重构KubeEdge官网 - -## 2023 H2 - -### SIG Node - -- 支持Windows边缘节点 -- 对边缘节点进行功能增强,如支持静态Pod、事件报告和可配置的应用迁移策略 -- 支持在RTOS系统上运行边缘节点 +- 持续跟进Kubernetes版本 +- 支持在mac、RTOS系统上运行边缘节点 +- 支持in-cluster config,以便边缘Pod访问 Kube-APIServer - 对节点上的设备插件进行改进,如支持多个虚拟GPU +- 节点侧event上报到云 - 支持边缘Serverless -- 远程升级边缘节点特性GA -- 优化节点组功能,如支持更多差异化配置参数 +- 远程运维 +- 批量纳管边缘节点 -### SIG Device-IOT +## SIG Device-IOT -- 支持DMI数据平面(H1已完成设计) -- 基于DMI的边缘设备在多节点间的迁移方案 -- 更新基于最新DMI框架的内置Mapper -- 多语言Mapper支持调研 +- 多语言Mapper支持 +- 设备发现 - 时序数据库等数据库的集成 -- 重构DeviceInstance和Device Model API -- 提高自定义消息传输的可靠性 +- 流数据 +- 增强设备管理能力,如设备写入、设备状态监控 -### SIG Security +## SIG Security - SLSA / CodeQL(要达到SLSA L4仍有一些工作要做) - Spiffe调研 - 提供通用接口,支持多种加密算法证书 +- 云边消息通道增加认证鉴权 -### SIG Scalability +## SIG Scalability - 集成EdgeMesh规模和性能测试 - 针对IoT设备场景的规模和性能测试 -### Stability +## Stability - CloudCore的稳定性维护,包括稳定性测试和问题修复 - EdgeMesh稳定性 - 提高云边协同可靠性,如改进边缘 Kube-API接口和logs/exec等稳定性 -### SIG Testing - -- 单元测试覆盖率提升 -- 基于场景的e2e测试用例覆盖率提升 -- 集成测试 -- 运行时和K8s版本兼容性测试 -- Keadm跨版本兼容性测试 -- 云-边缘跨版本兼容性测试 - -### SIG Networking +## SIG Networking -- 节点离线优化 - - 某节点离线后,其他节点收到ep里对应节点后端被摘除 +- 服务网格 + - 与istio或kmesh等项目结合,为边缘场景带来更丰富的Service Mesh功能。 - 大规模优化 - - 在大规模部署场景中,边缘kube apiserver的负载很高,考虑使用IPVS(IP虚拟服务器)技术来有效处理请求 - - 具有大量服务的情况下,会大大增加了节点上的iptables规则数 - - 容器网络支持CNI特性 -- 性能优化:基于eBPF(扩展伯克利数据包过滤器)的内核流量转发 + - 在大规模部署场景中,边缘kube apiserver的负载很高,考虑使用IPVS(IP虚拟服务器)技术来有效处理请求 + - 具有大量服务的情况下,会大大增加节点上的iptables规则数 +- 性能优化:基于eBPF(扩展伯克利包过滤器)的内核级流量转发 - 分布式消息系统 -### SIG Cluster-Lifecycle +## SIG AI -- 支持Windows安装和部署 -- 支持边缘应用镜像预下载 -- 消息路由高可用性(HA)支持 +- LLM模型的分布式部署 + - 使用 KubeEdge 在多个边缘节点上部署大型语言模型 (LLM)。 LLM可用于各种自然语言处理任务,例如代码实现、文本生成、机器翻译、摘要等 + - 分布式部署可以减少LLM的计算消耗,并提高其可扩展性和容错能力 +- LLM模型的云边基准测试 + - 比较在边缘节点上运行的 LLM 模型与使用 KubeEdge 的云服务器上运行的 LLM 模型的性能和资源消耗。 LLM 可以根据不同的指标进行评估,例如准确性、速度、内存、CPU 等 + - 基准测试可以帮助针对不同场景和环境优化LLM模型,并确定云边协同的权衡和挑战 +- 不同类型LLM模型的整合 + - 将不同类型的LLM模型(例如大语言/视觉/多模态模型)与KubeEdge集成。 LLM模型可以组合以实现更复杂和多样化的语言生成和理解任务,例如问答、对话、图像字幕等 + - 集成可以充分利用各个LLM模型的优势,增强边云系统的整体功能和能力 -### Docs +## SIG Robotics -- 官网文档优化,包括目录重构以及完备性提升 -- 支持更新文档版本 -- 完善官网文档,包括DMI开发者指南、对接监控操作指南等 -- 在官网案例中心发布案例 +- 通用机器人控制系统 + - 机器人控制系统的标准协议已开源(https://github.com/kubeedge/robolink),未来将基于该标准实现通用的机器人控制系统 -### UI +## SIG Testing -- Dashboard版本迭代 -- 在官网新增案例中心 -- 在官网新增招聘中心 -- 支持官网的版本控制 +- 单元测试覆盖率提升 +- 基于场景的e2e测试用例覆盖率提升 +- 集成测试 +- 一致性测试改进 +- 对KubeEdge所需的硬件要求进行测试,例如内存使用情况、带宽及其他指标 -### SIG AI +## SIG Cluster-Lifecycle -- 支持边云协同生命周期学习中的半自动注释 -- 支持边云协同生命周期学习中的运行时未知任务处理 -- 支持边云协同生命周期学习中的高级离线未知任务处理 +- 消息路由高可用性(HA)支持 +- Keink、Keadm工具的增强 +- Edgecore配置可用于Keadm join +- 安装工具Keadm增强 +- 优化安装(keadm join)流程 +- 镜像预加载能力增强 +- 支持OTA模式 -### SIG Robotics +## UI -- 新增RoboDev仓库:为开发人员更轻松地构建机器人应用程序 -- 新增RTF(开箱即用)机器人端到端解决方案:Teleoperation(遥操作)和RoboPilot(具身智能框架) +- Dashboard版本迭代 -### Experience +## Experience - Example库增强 - 上线到Killer-Coda \ No newline at end of file From cf0face059805cf2564a921decebe67d4a50beb9 Mon Sep 17 00:00:00 2001 From: WillardHu Date: Wed, 19 Jun 2024 15:33:09 +0800 Subject: [PATCH 08/64] Add Wei Hu to the security groups Signed-off-by: WillardHu Signed-off-by: chch9907 <799783944@qq.com> --- team-security/security-groups.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/team-security/security-groups.md b/team-security/security-groups.md index f401282..b937f5e 100644 --- a/team-security/security-groups.md +++ b/team-security/security-groups.md @@ -26,9 +26,11 @@ Owners: - [wangzefeng@huawei.com](mailto:wangzefeng@huawei.com) - [xufei40@huawei.com](mailto:xufei40@huawei.com) - [linguohui1@huawei.com](mailto:linguohui1@huawei.com) +- [wei.hu@daocloud.io](mailto:wei.hu@daocloud.io) Members: - [wangzefeng@huawei.com](mailto:wangzefeng@huawei.com) - [xufei40@huawei.com](mailto:xufei40@huawei.com) - [linguohui1@huawei.com](mailto:linguohui1@huawei.com) +- [wei.hu@daocloud.io](mailto:wei.hu@daocloud.io) From 01bd86b1c72a38aae9b5c532f8d184a58c550e53 Mon Sep 17 00:00:00 2001 From: fisherxu Date: Mon, 24 Jun 2024 17:02:56 +0800 Subject: [PATCH 09/64] nominate chair and tech lead for SIG testing Signed-off-by: fisherxu Signed-off-by: chch9907 <799783944@qq.com> --- sig-testing/README.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/sig-testing/README.md b/sig-testing/README.md index c6b8dfe..484536d 100644 --- a/sig-testing/README.md +++ b/sig-testing/README.md @@ -20,11 +20,12 @@ The [charter](https://github.com/kubeedge/community/blob/master/sig-testing/char The Chairs of the SIG run operations and processes governing the SIG. -*TODO* +- Yue Li (@liyuerich), DaoCloud +- Shelley Bao (@Shelley-BaoYue), Huawei ### Technical Leads The Technical Leads of the SIG establish new subprojects, decommission existing subprojects, and resolve cross-subproject technical issues and decisions. - Shelley Bao (@Shelley-BaoYue), Huawei - +- Yue Li (@liyuerich), DaoCloud From ca865460667088dd3b4f2b02378ca040099d17ee Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Sat, 24 Aug 2024 12:41:57 +0800 Subject: [PATCH 10/64] Create Offline Visual Navigation without Metric Map.md add scale proposal Signed-off-by: chch9907 <799783944@qq.com> --- ...ne Visual Navigation without Metric Map.md | 64 +++++++++++++++++++ 1 file changed, 64 insertions(+) create mode 100644 sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md new file mode 100644 index 0000000..e6e852b --- /dev/null +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -0,0 +1,64 @@ +# Offline Visual Navigation without Metric Map + +## Background +Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. + +Through real-world testing, we observe that due to the limited state distribution in the offline dataset, when facing the **out-of-distribution (OOD)** observations induced by illumination and scenario changes and the accumulative errors, the localization loss issue often arises that the robot fails to localize itself on a given map. In this case, the robot may get stuck or make infeasible decision, which significantly decreases the navigation success rates and even results in collision. And this often requires tedious human monitoring and correction. To address this issue, our work poses the question and takes one step forward:’ can we leverage the prior knowledge from offline data to guide the robot to **self-correct** the trajectory autonomously without human intervention or specific data collection?’ To this end, our key idea is learning from offline dataset to predict whether future trajectories lie in the prior distribution, and plan the future trajectories that can drive the robot to the familiar places autonomously. Real-world experiments show our method achieves +26% average success rates and x2 longer the average distance until intervention than the baselines (ViNG and ReViND), proving that our method can significantly reduce the human intervention for practical applications. This project has been accepted by 2024 ICRA, see paper: https://arxiv.org/abs/2404.10675. + +## Goals + +For developers and end users, the goals of this project are: +- Building a navigation system with only visual input and without metric maps. The policy is trained by directly learning from real-world offline datasets that does not suffer from any sim-to-real gap. Furthermore, the system is robust to the OOD observations induced by illumination and scenario changes and accumulative error issues during navigation. +- Building a low-cost system without metric map by using purely vision input for supporting neural topological SLAM, which particularly can be easily integrated into an instruction-followed navigation tasks by grounding the languages into the visual semantics similar to LM-Nav [5]. +- Addressing the localization loss issue by the proposed self-correcting method so as to enable long-term navigation with little human intervention during the process. +- Providing an offline dataset collection method to enable the users to finetune on the new environments. + +## Proposal + +### Project scope +- This project considers the image-goal visual navigation task in both indoor and outdoor scenarios, which **inputs a RGB image and a specified goal image** and **outputs the actions (linear and angular velocities)** of the mobile robot at timestep t without LIDAR, GPS or IMU information. Its **downstream tasks** can include park delivery, mobile manipulation, or offline training a generic agent. + +- The method first collects some trajectories of the scene for pre-building a topological map where images as nodes and the number of traversing steps as edges for topological planning and localization. Then, the navigation system conducts image-goal conditioned travels in the scene while is robust against the localization lost issue. + +### Targeting users + +- Users that need a training pipeline of offline imitation learning or reinforcement learning that does not suffer from sim-to-real gap and can scale to handling larger datasets to achieve a generic AI agent similar to ViNT [3]. +- Users that need a visual navigation system in the deploying environments to implement image-goal navigation tasks such as last-mile delivering given an image as a goal. +- Users that need a non-metric map representation, i.e., an image-node topological graph for long-horizon planning and localization with low storing and updating cost. +- Developers that want to build a customized visual navigation systems for mobile or mobile manipulation tasks on the robots. + +## Design Details +### Architecture Design + + + +![framework](./images/framework.png "overall framework") +**Fig. 1. Overall framework.** (a) SCALE first pretrains a self-consistent representation space $z$ by the VAE-style loss, then fine-tunes it by the gradients from $Q(s, a, g)$ in the IQL. Next, we train the policy network $π$, temporal affordance model $ψ$ and the novelty predictor $f_ω$ over the trained representation space. (b) When the robot gets lost, SCALE randomly samples some transition $u$ from the prior $p(u)$ and feds it to the temporal affordance model to generate some multi-step latent trajectories recursively. Then it evaluates the candidates in terms of the reachability, anti-novelty and aggressiveness. Finally, it selects the optimal trajectory and executes the first step, then repeats until being successfully localized again. + + +The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned visual navigation policy and value function, 2) a neural topological SLAM module, and 3) a self-correcting module for autonomously recovering the localization. + +- **Image-goal conditioned visual navigation**: we use implicit Q-learning (IQL) [6], an offline reinforcement learning method to learn the value function and policy from offline dataset. In practice, we find two techniques useful for successfully learning the value functions from purely visual input: 1) *negative sampling* is necessary for value learning with paired image inputs, where we assign image pairs that are separated less than or equal to a threshold of timesteps $d_{max}$ as the positive samples $B_+$ and those separated beyond the threshold are assigned as the negative samples $B_−$. 2) *relative goal embedding*, the difference between goal and current image embeddings, *i.e.*, $∆z_{g,t} = z_g – z_t$, is more effective than directly using goal embedding $z_g$ as the input for the goalconditioned networks to perceive the goal orientation. +- **Self-correction for localization recovery**: we self-supervisedly learn an affordance model from offline dataset for generating potential future trajectories in the latent space. On the one hand, to predict multi-step trajectories, we use conditional generation model to learn the latent space with forward-inverse cycle consistency (FICC). On the other hand, we take advantage of the surrounding pixels in history frames using recurrent neural network (RNN) to predict some aggressive trajectories that are largely beyond the current field-of-view. Furthermore, we learn a novelty estimator via random network distillation (RND) [7] technique to evaluate the predicted future states. Intuitively, the reachability and aggressiveness facilitate the reasonability and diversity, while the anti-novelty strategy can induce the robot to the familiar places. +- **Neural topological SLAM**: we construct a topological map in the environment where each node is a previously observed image and each edge refers to the traversability between two nodes that is estimated by the learned value function. The localization process is conducted by searching the highest value prediction between current image and all the images on the map. An example of the integration of navigation, neural topological SLAM, and localization recovery module is illustrated in Fig.2. + ![topological_navigation](./images/topological_navigation.png "topological_navigation" ) + **Fig. 2. Topological navigation with localization recovery**. SCALE combines the topological visual navigation with a novel localization recovery module. + + + + + +### Data Collection Module +We manually teleoperate the mobile robot to collect the trajectory datasets in the deployed scenarios. Commonly, a totally 30-minute dataset in each scenario is enough for training the navigation policy. In each trajectory of the dataset, the data consists of an onboard RGB image $o_t$ and the estimated pose $p_t = (x, y, θ)$ from odometry at each timestep. Note that the estimated pose in offline dataset is only used to calculate the robot action for offline training, while we only use RGB image as the observation during inference. + + +## References +[1] D. Shah et al., “ViNG: Learning Open-World Navigation with Visual Goals”, 2021. +[2] D. Shah et al., “Offline Reinforcement Learning for Visual Navigation”, 2022. +[3] D. Shah et al., “ViNT: A Foundation Model for Visual Navigation”, 2023. +[4] D.S. Chaplot et al., “Neural Topological SLAM for Visual Navigation”, 2020 +[5] D. Shah et al., “LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action”, 2022 +[6] Kostrikov et al., “Offline Reinforcement Learning with Implicit Q-Learning”, 2021. +[7] Burda et al., “Exploration by Random Network Distillation”, 2018. From ce754894a8355f0c151bb21e41160cf3e0714213 Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Sat, 24 Aug 2024 13:08:10 +0800 Subject: [PATCH 11/64] add Offline Visual Navigation without Metric Map Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map/images/test.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt new file mode 100644 index 0000000..e8689b2 --- /dev/null +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt @@ -0,0 +1 @@ +hh From f1d0394c14659a83ed701427e486d1dabba4826d Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Sat, 24 Aug 2024 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    n@lYx96vXV*?4Pqvt{|C}P BWRU;> literal 0 HcmV?d00001 From b2982ecc008400b1443c11480347e86a5e3eab1b Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Sat, 24 Aug 2024 13:09:35 +0800 Subject: [PATCH 13/64] Delete sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map/images/test.txt | 1 - 1 file changed, 1 deletion(-) delete mode 100644 sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt deleted file mode 100644 index e8689b2..0000000 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt +++ /dev/null @@ -1 +0,0 @@ -hh From 2f5cbf84083d3252109f854b078b511242ddd393 Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Sat, 24 Aug 2024 13:16:53 +0800 Subject: [PATCH 14/64] Update Offline Visual Navigation without Metric Map.md Signed-off-by: chch9907 <799783944@qq.com> --- ...ine Visual Navigation without Metric Map.md | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index e6e852b..2f39346 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -3,7 +3,8 @@ ## Background Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. -Through real-world testing, we observe that due to the limited state distribution in the offline dataset, when facing the **out-of-distribution (OOD)** observations induced by illumination and scenario changes and the accumulative errors, the localization loss issue often arises that the robot fails to localize itself on a given map. In this case, the robot may get stuck or make infeasible decision, which significantly decreases the navigation success rates and even results in collision. And this often requires tedious human monitoring and correction. To address this issue, our work poses the question and takes one step forward:’ can we leverage the prior knowledge from offline data to guide the robot to **self-correct** the trajectory autonomously without human intervention or specific data collection?’ To this end, our key idea is learning from offline dataset to predict whether future trajectories lie in the prior distribution, and plan the future trajectories that can drive the robot to the familiar places autonomously. Real-world experiments show our method achieves +26% average success rates and x2 longer the average distance until intervention than the baselines (ViNG and ReViND), proving that our method can significantly reduce the human intervention for practical applications. This project has been accepted by 2024 ICRA, see paper: https://arxiv.org/abs/2404.10675. +Through real-world testing, we observe that due to the limited state distribution in the offline dataset, when facing the **out-of-distribution (OOD)** observations induced by illumination and scenario changes and the accumulative errors, the localization loss issue often arises that the robot fails to localize itself on a given map. In this case, the robot may get stuck or make infeasible decision, which significantly decreases the navigation success rates and even results in collision. And this often requires tedious human monitoring and correction. To address this issue, our work poses the question and takes one step forward:’ can we leverage the prior knowledge from offline data to guide the robot to **self-correct** the trajectory autonomously without human intervention or specific data collection?’ To this end, our key idea is learning from offline dataset to predict whether future trajectories lie in the prior distribution, and plan the future trajectories that can drive the robot to the familiar places autonomously. Real-world experiments show our method achieves +26% average success rates and x2 longer the average distance until intervention than the baselines (ViNG and ReViND), proving that our method can significantly reduce the human intervention for practical applications. This project, called "*SCALE: Self-Correcting Visual Navigation for Mobile Robots via +Anti-Novelty Estimation*", has been accepted by 2024 ICRA, see paper: https://arxiv.org/abs/2404.10675. ## Goals @@ -41,11 +42,18 @@ The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned - **Image-goal conditioned visual navigation**: we use implicit Q-learning (IQL) [6], an offline reinforcement learning method to learn the value function and policy from offline dataset. In practice, we find two techniques useful for successfully learning the value functions from purely visual input: 1) *negative sampling* is necessary for value learning with paired image inputs, where we assign image pairs that are separated less than or equal to a threshold of timesteps $d_{max}$ as the positive samples $B_+$ and those separated beyond the threshold are assigned as the negative samples $B_−$. 2) *relative goal embedding*, the difference between goal and current image embeddings, *i.e.*, $∆z_{g,t} = z_g – z_t$, is more effective than directly using goal embedding $z_g$ as the input for the goalconditioned networks to perceive the goal orientation. - **Self-correction for localization recovery**: we self-supervisedly learn an affordance model from offline dataset for generating potential future trajectories in the latent space. On the one hand, to predict multi-step trajectories, we use conditional generation model to learn the latent space with forward-inverse cycle consistency (FICC). On the other hand, we take advantage of the surrounding pixels in history frames using recurrent neural network (RNN) to predict some aggressive trajectories that are largely beyond the current field-of-view. Furthermore, we learn a novelty estimator via random network distillation (RND) [7] technique to evaluate the predicted future states. Intuitively, the reachability and aggressiveness facilitate the reasonability and diversity, while the anti-novelty strategy can induce the robot to the familiar places. - **Neural topological SLAM**: we construct a topological map in the environment where each node is a previously observed image and each edge refers to the traversability between two nodes that is estimated by the learned value function. The localization process is conducted by searching the highest value prediction between current image and all the images on the map. An example of the integration of navigation, neural topological SLAM, and localization recovery module is illustrated in Fig.2. - ![topological_navigation](./images/topological_navigation.png "topological_navigation" ) +

    + **Fig. 2. Topological navigation with localization recovery**. SCALE combines the topological visual navigation with a novel localization recovery module. - + + + + From 9a94c8baf6310a96afde557e04062a9658dcd36b Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Wed, 28 Aug 2024 12:15:35 +0800 Subject: [PATCH 15/64] Update sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md Co-authored-by: Tomoya Fujita Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index 2f39346..a020a34 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -34,8 +34,9 @@ For developers and end users, the goals of this project are: ![framework](./images/framework.png "overall framework") -**Fig. 1. Overall framework.** (a) SCALE first pretrains a self-consistent representation space $z$ by the VAE-style loss, then fine-tunes it by the gradients from $Q(s, a, g)$ in the IQL. Next, we train the policy network $π$, temporal affordance model $ψ$ and the novelty predictor $f_ω$ over the trained representation space. (b) When the robot gets lost, SCALE randomly samples some transition $u$ from the prior $p(u)$ and feds it to the temporal affordance model to generate some multi-step latent trajectories recursively. Then it evaluates the candidates in terms of the reachability, anti-novelty and aggressiveness. Finally, it selects the optimal trajectory and executes the first step, then repeats until being successfully localized again. - +**Fig. 1. Overall framework.** +(a) SCALE first pretrains a self-consistent representation space $z$ by the VAE-style loss, then fine-tunes it by the gradients from $Q(s, a, g)$ in the IQL. Next, we train the policy network $π$, temporal affordance model $ψ$ and the novelty predictor $f_ω$ over the trained representation space. +(b) When the robot gets lost, SCALE randomly samples some transition $u$ from the prior $p(u)$ and feds it to the temporal affordance model to generate some multi-step latent trajectories recursively. Then it evaluates the candidates in terms of the reachability, anti-novelty and aggressiveness. Finally, it selects the optimal trajectory and executes the first step, then repeats until being successfully localized again. The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned visual navigation policy and value function, 2) a neural topological SLAM module, and 3) a self-correcting module for autonomously recovering the localization. From f62c4734f315e07cf32258a53684543f9da734e5 Mon Sep 17 00:00:00 2001 From: Poorunga <2744323@qq.com> Date: Thu, 7 Sep 2023 19:30:32 +0800 Subject: [PATCH 16/64] add SIG Networking chairs Signed-off-by: Poorunga <2744323@qq.com> Signed-off-by: chch9907 <799783944@qq.com> --- sig-networking/README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/sig-networking/README.md b/sig-networking/README.md index 3cef97a..b4e7517 100644 --- a/sig-networking/README.md +++ b/sig-networking/README.md @@ -21,7 +21,8 @@ The [charter](charter.md) defines the scope and governance of the Networking Spe The Chairs of the SIG run operations and processes governing the SIG. -*TODO* +- Jiawei Liu (@JiaweiGithub), DaoCloud +- Jiezhang Wang (@Poorunga), Huawei ### Technical Leads From 97142b4ead9cf6b417b64b9162361e552d9d0d6e Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Wed, 28 Aug 2024 12:16:47 +0800 Subject: [PATCH 17/64] Apply suggestions from code review Co-authored-by: Tomoya Fujita Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map.md | 8 -------- 1 file changed, 8 deletions(-) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index a020a34..8802085 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -31,8 +31,6 @@ For developers and end users, the goals of this project are: ## Design Details ### Architecture Design - - ![framework](./images/framework.png "overall framework") **Fig. 1. Overall framework.** (a) SCALE first pretrains a self-consistent representation space $z$ by the VAE-style loss, then fine-tunes it by the gradients from $Q(s, a, g)$ in the IQL. Next, we train the policy network $π$, temporal affordance model $ψ$ and the novelty predictor $f_ω$ over the trained representation space. @@ -54,15 +52,9 @@ The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned ![topological_navigation](./images/topological_navigation.png "topological_navigation" )--> - - - - - ### Data Collection Module We manually teleoperate the mobile robot to collect the trajectory datasets in the deployed scenarios. Commonly, a totally 30-minute dataset in each scenario is enough for training the navigation policy. In each trajectory of the dataset, the data consists of an onboard RGB image $o_t$ and the estimated pose $p_t = (x, y, θ)$ from odometry at each timestep. Note that the estimated pose in offline dataset is only used to calculate the robot action for offline training, while we only use RGB image as the observation during inference. - ## References [1] D. Shah et al., “ViNG: Learning Open-World Navigation with Visual Goals”, 2021. [2] D. Shah et al., “Offline Reinforcement Learning for Visual Navigation”, 2022. From f85f4ee3c556ae258af2f00b128ce98642cdb9ed Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Thu, 29 Aug 2024 13:11:03 +0800 Subject: [PATCH 18/64] Update sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md Co-authored-by: Tomoya Fujita Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map.md | 1 + 1 file changed, 1 insertion(+) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index 8802085..4af6e96 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -17,6 +17,7 @@ For developers and end users, the goals of this project are: ## Proposal ### Project scope + - This project considers the image-goal visual navigation task in both indoor and outdoor scenarios, which **inputs a RGB image and a specified goal image** and **outputs the actions (linear and angular velocities)** of the mobile robot at timestep t without LIDAR, GPS or IMU information. Its **downstream tasks** can include park delivery, mobile manipulation, or offline training a generic agent. - The method first collects some trajectories of the scene for pre-building a topological map where images as nodes and the number of traversing steps as edges for topological planning and localization. Then, the navigation system conducts image-goal conditioned travels in the scene while is robust against the localization lost issue. From 07339de123c9fa5ce9da5a33990caf80bac0ab41 Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Thu, 29 Aug 2024 13:11:23 +0800 Subject: [PATCH 19/64] Update sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md Co-authored-by: Tomoya Fujita Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map.md | 1 + 1 file changed, 1 insertion(+) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index 4af6e96..e2f5dba 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -1,6 +1,7 @@ # Offline Visual Navigation without Metric Map ## Background + Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. Through real-world testing, we observe that due to the limited state distribution in the offline dataset, when facing the **out-of-distribution (OOD)** observations induced by illumination and scenario changes and the accumulative errors, the localization loss issue often arises that the robot fails to localize itself on a given map. In this case, the robot may get stuck or make infeasible decision, which significantly decreases the navigation success rates and even results in collision. And this often requires tedious human monitoring and correction. To address this issue, our work poses the question and takes one step forward:’ can we leverage the prior knowledge from offline data to guide the robot to **self-correct** the trajectory autonomously without human intervention or specific data collection?’ To this end, our key idea is learning from offline dataset to predict whether future trajectories lie in the prior distribution, and plan the future trajectories that can drive the robot to the familiar places autonomously. Real-world experiments show our method achieves +26% average success rates and x2 longer the average distance until intervention than the baselines (ViNG and ReViND), proving that our method can significantly reduce the human intervention for practical applications. This project, called "*SCALE: Self-Correcting Visual Navigation for Mobile Robots via From 55421a9a42b7ec9b2db7996927689b0607755066 Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Thu, 29 Aug 2024 13:11:29 +0800 Subject: [PATCH 20/64] Update sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md Co-authored-by: Tomoya Fujita Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index e2f5dba..0307d0d 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -2,7 +2,7 @@ ## Background -Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. +Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route has been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. Through real-world testing, we observe that due to the limited state distribution in the offline dataset, when facing the **out-of-distribution (OOD)** observations induced by illumination and scenario changes and the accumulative errors, the localization loss issue often arises that the robot fails to localize itself on a given map. In this case, the robot may get stuck or make infeasible decision, which significantly decreases the navigation success rates and even results in collision. And this often requires tedious human monitoring and correction. To address this issue, our work poses the question and takes one step forward:’ can we leverage the prior knowledge from offline data to guide the robot to **self-correct** the trajectory autonomously without human intervention or specific data collection?’ To this end, our key idea is learning from offline dataset to predict whether future trajectories lie in the prior distribution, and plan the future trajectories that can drive the robot to the familiar places autonomously. Real-world experiments show our method achieves +26% average success rates and x2 longer the average distance until intervention than the baselines (ViNG and ReViND), proving that our method can significantly reduce the human intervention for practical applications. This project, called "*SCALE: Self-Correcting Visual Navigation for Mobile Robots via Anti-Novelty Estimation*", has been accepted by 2024 ICRA, see paper: https://arxiv.org/abs/2404.10675. From 324eb67ee4ecb96de912ff2e8232490f1645adf7 Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Thu, 29 Aug 2024 13:11:37 +0800 Subject: [PATCH 21/64] Update sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md Co-authored-by: Tomoya Fujita Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map.md | 1 + 1 file changed, 1 insertion(+) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index 0307d0d..8b1d7c3 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -55,6 +55,7 @@ The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned ![topological_navigation](./images/topological_navigation.png "topological_navigation" )--> ### Data Collection Module + We manually teleoperate the mobile robot to collect the trajectory datasets in the deployed scenarios. Commonly, a totally 30-minute dataset in each scenario is enough for training the navigation policy. In each trajectory of the dataset, the data consists of an onboard RGB image $o_t$ and the estimated pose $p_t = (x, y, θ)$ from odometry at each timestep. Note that the estimated pose in offline dataset is only used to calculate the robot action for offline training, while we only use RGB image as the observation during inference. ## References From 1fa2bb87886aa5f893e3c6b93e17b7ee2a7e563d Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Sat, 24 Aug 2024 12:41:57 +0800 Subject: [PATCH 22/64] Create Offline Visual Navigation without Metric Map.md add scale proposal Signed-off-by: chch9907 <799783944@qq.com> --- ...ne Visual Navigation without Metric Map.md | 80 +++++++++++++++++-- 1 file changed, 74 insertions(+), 6 deletions(-) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index 8b1d7c3..cba3863 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -1,8 +1,7 @@ # Offline Visual Navigation without Metric Map ## Background - -Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route has been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. +Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. Through real-world testing, we observe that due to the limited state distribution in the offline dataset, when facing the **out-of-distribution (OOD)** observations induced by illumination and scenario changes and the accumulative errors, the localization loss issue often arises that the robot fails to localize itself on a given map. In this case, the robot may get stuck or make infeasible decision, which significantly decreases the navigation success rates and even results in collision. And this often requires tedious human monitoring and correction. To address this issue, our work poses the question and takes one step forward:’ can we leverage the prior knowledge from offline data to guide the robot to **self-correct** the trajectory autonomously without human intervention or specific data collection?’ To this end, our key idea is learning from offline dataset to predict whether future trajectories lie in the prior distribution, and plan the future trajectories that can drive the robot to the familiar places autonomously. Real-world experiments show our method achieves +26% average success rates and x2 longer the average distance until intervention than the baselines (ViNG and ReViND), proving that our method can significantly reduce the human intervention for practical applications. This project, called "*SCALE: Self-Correcting Visual Navigation for Mobile Robots via Anti-Novelty Estimation*", has been accepted by 2024 ICRA, see paper: https://arxiv.org/abs/2404.10675. @@ -18,7 +17,6 @@ For developers and end users, the goals of this project are: ## Proposal ### Project scope - - This project considers the image-goal visual navigation task in both indoor and outdoor scenarios, which **inputs a RGB image and a specified goal image** and **outputs the actions (linear and angular velocities)** of the mobile robot at timestep t without LIDAR, GPS or IMU information. Its **downstream tasks** can include park delivery, mobile manipulation, or offline training a generic agent. - The method first collects some trajectories of the scene for pre-building a topological map where images as nodes and the number of traversing steps as edges for topological planning and localization. Then, the navigation system conducts image-goal conditioned travels in the scene while is robust against the localization lost issue. @@ -33,10 +31,11 @@ For developers and end users, the goals of this project are: ## Design Details ### Architecture Design + + ![framework](./images/framework.png "overall framework") -**Fig. 1. Overall framework.** -(a) SCALE first pretrains a self-consistent representation space $z$ by the VAE-style loss, then fine-tunes it by the gradients from $Q(s, a, g)$ in the IQL. Next, we train the policy network $π$, temporal affordance model $ψ$ and the novelty predictor $f_ω$ over the trained representation space. -(b) When the robot gets lost, SCALE randomly samples some transition $u$ from the prior $p(u)$ and feds it to the temporal affordance model to generate some multi-step latent trajectories recursively. Then it evaluates the candidates in terms of the reachability, anti-novelty and aggressiveness. Finally, it selects the optimal trajectory and executes the first step, then repeats until being successfully localized again. +**Fig. 1. Overall framework.** (a) SCALE first pretrains a self-consistent representation space $z$ by the VAE-style loss, then fine-tunes it by the gradients from $Q(s, a, g)$ in the IQL. Next, we train the policy network $π$, temporal affordance model $ψ$ and the novelty predictor $f_ω$ over the trained representation space. (b) When the robot gets lost, SCALE randomly samples some transition $u$ from the prior $p(u)$ and feds it to the temporal affordance model to generate some multi-step latent trajectories recursively. Then it evaluates the candidates in terms of the reachability, anti-novelty and aggressiveness. Finally, it selects the optimal trajectory and executes the first step, then repeats until being successfully localized again. + The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned visual navigation policy and value function, 2) a neural topological SLAM module, and 3) a self-correcting module for autonomously recovering the localization. @@ -54,10 +53,79 @@ The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned ![topological_navigation](./images/topological_navigation.png "topological_navigation" )--> + + + + + ### Data Collection Module +We manually teleoperate the mobile robot to collect the trajectory datasets in the deployed scenarios. Commonly, a totally 30-minute dataset in each scenario is enough for training the navigation policy. In each trajectory of the dataset, the data consists of an onboard RGB image $o_t$ and the estimated pose $p_t = (x, y, θ)$ from odometry at each timestep. Note that the estimated pose in offline dataset is only used to calculate the robot action for offline training, while we only use RGB image as the observation during inference. + + +## References +[1] D. Shah et al., “ViNG: Learning Open-World Navigation with Visual Goals”, 2021. +[2] D. Shah et al., “Offline Reinforcement Learning for Visual Navigation”, 2022. +[3] D. Shah et al., “ViNT: A Foundation Model for Visual Navigation”, 2023. +[4] D.S. Chaplot et al., “Neural Topological SLAM for Visual Navigation”, 2020 +[5] D. Shah et al., “LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action”, 2022 +[6] Kostrikov et al., “Offline Reinforcement Learning with Implicit Q-Learning”, 2021. +[7] Burda et al., “Exploration by Random Network Distillation”, 2018. +# Offline Visual Navigation without Metric Map + +## Background +Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. + +Through real-world testing, we observe that due to the limited state distribution in the offline dataset, when facing the **out-of-distribution (OOD)** observations induced by illumination and scenario changes and the accumulative errors, the localization loss issue often arises that the robot fails to localize itself on a given map. In this case, the robot may get stuck or make infeasible decision, which significantly decreases the navigation success rates and even results in collision. And this often requires tedious human monitoring and correction. To address this issue, our work poses the question and takes one step forward:’ can we leverage the prior knowledge from offline data to guide the robot to **self-correct** the trajectory autonomously without human intervention or specific data collection?’ To this end, our key idea is learning from offline dataset to predict whether future trajectories lie in the prior distribution, and plan the future trajectories that can drive the robot to the familiar places autonomously. Real-world experiments show our method achieves +26% average success rates and x2 longer the average distance until intervention than the baselines (ViNG and ReViND), proving that our method can significantly reduce the human intervention for practical applications. This project has been accepted by 2024 ICRA, see paper: https://arxiv.org/abs/2404.10675. + +## Goals + +For developers and end users, the goals of this project are: +- Building a navigation system with only visual input and without metric maps. The policy is trained by directly learning from real-world offline datasets that does not suffer from any sim-to-real gap. Furthermore, the system is robust to the OOD observations induced by illumination and scenario changes and accumulative error issues during navigation. +- Building a low-cost system without metric map by using purely vision input for supporting neural topological SLAM, which particularly can be easily integrated into an instruction-followed navigation tasks by grounding the languages into the visual semantics similar to LM-Nav [5]. +- Addressing the localization loss issue by the proposed self-correcting method so as to enable long-term navigation with little human intervention during the process. +- Providing an offline dataset collection method to enable the users to finetune on the new environments. + +## Proposal + +### Project scope +- This project considers the image-goal visual navigation task in both indoor and outdoor scenarios, which **inputs a RGB image and a specified goal image** and **outputs the actions (linear and angular velocities)** of the mobile robot at timestep t without LIDAR, GPS or IMU information. Its **downstream tasks** can include park delivery, mobile manipulation, or offline training a generic agent. + +- The method first collects some trajectories of the scene for pre-building a topological map where images as nodes and the number of traversing steps as edges for topological planning and localization. Then, the navigation system conducts image-goal conditioned travels in the scene while is robust against the localization lost issue. + +### Targeting users + +- Users that need a training pipeline of offline imitation learning or reinforcement learning that does not suffer from sim-to-real gap and can scale to handling larger datasets to achieve a generic AI agent similar to ViNT [3]. +- Users that need a visual navigation system in the deploying environments to implement image-goal navigation tasks such as last-mile delivering given an image as a goal. +- Users that need a non-metric map representation, i.e., an image-node topological graph for long-horizon planning and localization with low storing and updating cost. +- Developers that want to build a customized visual navigation systems for mobile or mobile manipulation tasks on the robots. + +## Design Details +### Architecture Design + + +![framework](./images/framework.png "overall framework") +**Fig. 1. Overall framework.** (a) SCALE first pretrains a self-consistent representation space $z$ by the VAE-style loss, then fine-tunes it by the gradients from $Q(s, a, g)$ in the IQL. Next, we train the policy network $π$, temporal affordance model $ψ$ and the novelty predictor $f_ω$ over the trained representation space. (b) When the robot gets lost, SCALE randomly samples some transition $u$ from the prior $p(u)$ and feds it to the temporal affordance model to generate some multi-step latent trajectories recursively. Then it evaluates the candidates in terms of the reachability, anti-novelty and aggressiveness. Finally, it selects the optimal trajectory and executes the first step, then repeats until being successfully localized again. + + +The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned visual navigation policy and value function, 2) a neural topological SLAM module, and 3) a self-correcting module for autonomously recovering the localization. + +- **Image-goal conditioned visual navigation**: we use implicit Q-learning (IQL) [6], an offline reinforcement learning method to learn the value function and policy from offline dataset. In practice, we find two techniques useful for successfully learning the value functions from purely visual input: 1) *negative sampling* is necessary for value learning with paired image inputs, where we assign image pairs that are separated less than or equal to a threshold of timesteps $d_{max}$ as the positive samples $B_+$ and those separated beyond the threshold are assigned as the negative samples $B_−$. 2) *relative goal embedding*, the difference between goal and current image embeddings, *i.e.*, $∆z_{g,t} = z_g – z_t$, is more effective than directly using goal embedding $z_g$ as the input for the goalconditioned networks to perceive the goal orientation. +- **Self-correction for localization recovery**: we self-supervisedly learn an affordance model from offline dataset for generating potential future trajectories in the latent space. On the one hand, to predict multi-step trajectories, we use conditional generation model to learn the latent space with forward-inverse cycle consistency (FICC). On the other hand, we take advantage of the surrounding pixels in history frames using recurrent neural network (RNN) to predict some aggressive trajectories that are largely beyond the current field-of-view. Furthermore, we learn a novelty estimator via random network distillation (RND) [7] technique to evaluate the predicted future states. Intuitively, the reachability and aggressiveness facilitate the reasonability and diversity, while the anti-novelty strategy can induce the robot to the familiar places. +- **Neural topological SLAM**: we construct a topological map in the environment where each node is a previously observed image and each edge refers to the traversability between two nodes that is estimated by the learned value function. The localization process is conducted by searching the highest value prediction between current image and all the images on the map. An example of the integration of navigation, neural topological SLAM, and localization recovery module is illustrated in Fig.2. + ![topological_navigation](./images/topological_navigation.png "topological_navigation" ) + **Fig. 2. Topological navigation with localization recovery**. SCALE combines the topological visual navigation with a novel localization recovery module. + + + + + +### Data Collection Module We manually teleoperate the mobile robot to collect the trajectory datasets in the deployed scenarios. Commonly, a totally 30-minute dataset in each scenario is enough for training the navigation policy. In each trajectory of the dataset, the data consists of an onboard RGB image $o_t$ and the estimated pose $p_t = (x, y, θ)$ from odometry at each timestep. Note that the estimated pose in offline dataset is only used to calculate the robot action for offline training, while we only use RGB image as the observation during inference. + ## References [1] D. Shah et al., “ViNG: Learning Open-World Navigation with Visual Goals”, 2021. [2] D. Shah et al., “Offline Reinforcement Learning for Visual Navigation”, 2022. From 867ec3c3c636fb835545f5cb623462e7d2ed920f Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Sat, 24 Aug 2024 13:08:10 +0800 Subject: [PATCH 23/64] add Offline Visual Navigation without Metric Map Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map/images/test.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt new file mode 100644 index 0000000..e8689b2 --- /dev/null +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt @@ -0,0 +1 @@ +hh From e7e1d92224cbe8d2d51c79eb0d23edb19f7972aa Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Sat, 24 Aug 2024 13:09:35 +0800 Subject: [PATCH 24/64] Delete sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map/images/test.txt | 1 - 1 file changed, 1 deletion(-) delete mode 100644 sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt deleted file mode 100644 index e8689b2..0000000 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt +++ /dev/null @@ -1 +0,0 @@ -hh From 9bf5da7f2f44e0843c875a4d0f724ef519dad486 Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Sat, 24 Aug 2024 13:16:53 +0800 Subject: [PATCH 25/64] Update Offline Visual Navigation without Metric Map.md Signed-off-by: chch9907 <799783944@qq.com> --- ...ne Visual Navigation without Metric Map.md | 90 +++++++++++++++++-- 1 file changed, 85 insertions(+), 5 deletions(-) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index cba3863..c497d50 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -75,7 +75,8 @@ We manually teleoperate the mobile robot to collect the trajectory datasets in t ## Background Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. -Through real-world testing, we observe that due to the limited state distribution in the offline dataset, when facing the **out-of-distribution (OOD)** observations induced by illumination and scenario changes and the accumulative errors, the localization loss issue often arises that the robot fails to localize itself on a given map. In this case, the robot may get stuck or make infeasible decision, which significantly decreases the navigation success rates and even results in collision. And this often requires tedious human monitoring and correction. To address this issue, our work poses the question and takes one step forward:’ can we leverage the prior knowledge from offline data to guide the robot to **self-correct** the trajectory autonomously without human intervention or specific data collection?’ To this end, our key idea is learning from offline dataset to predict whether future trajectories lie in the prior distribution, and plan the future trajectories that can drive the robot to the familiar places autonomously. Real-world experiments show our method achieves +26% average success rates and x2 longer the average distance until intervention than the baselines (ViNG and ReViND), proving that our method can significantly reduce the human intervention for practical applications. This project has been accepted by 2024 ICRA, see paper: https://arxiv.org/abs/2404.10675. +Through real-world testing, we observe that due to the limited state distribution in the offline dataset, when facing the **out-of-distribution (OOD)** observations induced by illumination and scenario changes and the accumulative errors, the localization loss issue often arises that the robot fails to localize itself on a given map. In this case, the robot may get stuck or make infeasible decision, which significantly decreases the navigation success rates and even results in collision. And this often requires tedious human monitoring and correction. To address this issue, our work poses the question and takes one step forward:’ can we leverage the prior knowledge from offline data to guide the robot to **self-correct** the trajectory autonomously without human intervention or specific data collection?’ To this end, our key idea is learning from offline dataset to predict whether future trajectories lie in the prior distribution, and plan the future trajectories that can drive the robot to the familiar places autonomously. Real-world experiments show our method achieves +26% average success rates and x2 longer the average distance until intervention than the baselines (ViNG and ReViND), proving that our method can significantly reduce the human intervention for practical applications. This project, called "*SCALE: Self-Correcting Visual Navigation for Mobile Robots via +Anti-Novelty Estimation*", has been accepted by 2024 ICRA, see paper: https://arxiv.org/abs/2404.10675. ## Goals @@ -113,11 +114,90 @@ The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned - **Image-goal conditioned visual navigation**: we use implicit Q-learning (IQL) [6], an offline reinforcement learning method to learn the value function and policy from offline dataset. In practice, we find two techniques useful for successfully learning the value functions from purely visual input: 1) *negative sampling* is necessary for value learning with paired image inputs, where we assign image pairs that are separated less than or equal to a threshold of timesteps $d_{max}$ as the positive samples $B_+$ and those separated beyond the threshold are assigned as the negative samples $B_−$. 2) *relative goal embedding*, the difference between goal and current image embeddings, *i.e.*, $∆z_{g,t} = z_g – z_t$, is more effective than directly using goal embedding $z_g$ as the input for the goalconditioned networks to perceive the goal orientation. - **Self-correction for localization recovery**: we self-supervisedly learn an affordance model from offline dataset for generating potential future trajectories in the latent space. On the one hand, to predict multi-step trajectories, we use conditional generation model to learn the latent space with forward-inverse cycle consistency (FICC). On the other hand, we take advantage of the surrounding pixels in history frames using recurrent neural network (RNN) to predict some aggressive trajectories that are largely beyond the current field-of-view. Furthermore, we learn a novelty estimator via random network distillation (RND) [7] technique to evaluate the predicted future states. Intuitively, the reachability and aggressiveness facilitate the reasonability and diversity, while the anti-novelty strategy can induce the robot to the familiar places. - **Neural topological SLAM**: we construct a topological map in the environment where each node is a previously observed image and each edge refers to the traversability between two nodes that is estimated by the learned value function. The localization process is conducted by searching the highest value prediction between current image and all the images on the map. An example of the integration of navigation, neural topological SLAM, and localization recovery module is illustrated in Fig.2. - ![topological_navigation](./images/topological_navigation.png "topological_navigation" ) +
    +architecture +
    + **Fig. 2. Topological navigation with localization recovery**. SCALE combines the topological visual navigation with a novel localization recovery module. - + + + + + + + + +### Data Collection Module +We manually teleoperate the mobile robot to collect the trajectory datasets in the deployed scenarios. Commonly, a totally 30-minute dataset in each scenario is enough for training the navigation policy. In each trajectory of the dataset, the data consists of an onboard RGB image $o_t$ and the estimated pose $p_t = (x, y, θ)$ from odometry at each timestep. Note that the estimated pose in offline dataset is only used to calculate the robot action for offline training, while we only use RGB image as the observation during inference. + + +## References +[1] D. Shah et al., “ViNG: Learning Open-World Navigation with Visual Goals”, 2021. +[2] D. Shah et al., “Offline Reinforcement Learning for Visual Navigation”, 2022. +[3] D. Shah et al., “ViNT: A Foundation Model for Visual Navigation”, 2023. +[4] D.S. Chaplot et al., “Neural Topological SLAM for Visual Navigation”, 2020 +[5] D. Shah et al., “LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action”, 2022 +[6] Kostrikov et al., “Offline Reinforcement Learning with Implicit Q-Learning”, 2021. +[7] Burda et al., “Exploration by Random Network Distillation”, 2018. +# Offline Visual Navigation without Metric Map + +## Background +Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. + +Through real-world testing, we observe that due to the limited state distribution in the offline dataset, when facing the **out-of-distribution (OOD)** observations induced by illumination and scenario changes and the accumulative errors, the localization loss issue often arises that the robot fails to localize itself on a given map. In this case, the robot may get stuck or make infeasible decision, which significantly decreases the navigation success rates and even results in collision. And this often requires tedious human monitoring and correction. To address this issue, our work poses the question and takes one step forward:’ can we leverage the prior knowledge from offline data to guide the robot to **self-correct** the trajectory autonomously without human intervention or specific data collection?’ To this end, our key idea is learning from offline dataset to predict whether future trajectories lie in the prior distribution, and plan the future trajectories that can drive the robot to the familiar places autonomously. Real-world experiments show our method achieves +26% average success rates and x2 longer the average distance until intervention than the baselines (ViNG and ReViND), proving that our method can significantly reduce the human intervention for practical applications. This project, called "*SCALE: Self-Correcting Visual Navigation for Mobile Robots via +Anti-Novelty Estimation*", has been accepted by 2024 ICRA, see paper: https://arxiv.org/abs/2404.10675. + +## Goals + +For developers and end users, the goals of this project are: +- Building a navigation system with only visual input and without metric maps. The policy is trained by directly learning from real-world offline datasets that does not suffer from any sim-to-real gap. Furthermore, the system is robust to the OOD observations induced by illumination and scenario changes and accumulative error issues during navigation. +- Building a low-cost system without metric map by using purely vision input for supporting neural topological SLAM, which particularly can be easily integrated into an instruction-followed navigation tasks by grounding the languages into the visual semantics similar to LM-Nav [5]. +- Addressing the localization loss issue by the proposed self-correcting method so as to enable long-term navigation with little human intervention during the process. +- Providing an offline dataset collection method to enable the users to finetune on the new environments. + +## Proposal + +### Project scope +- This project considers the image-goal visual navigation task in both indoor and outdoor scenarios, which **inputs a RGB image and a specified goal image** and **outputs the actions (linear and angular velocities)** of the mobile robot at timestep t without LIDAR, GPS or IMU information. Its **downstream tasks** can include park delivery, mobile manipulation, or offline training a generic agent. + +- The method first collects some trajectories of the scene for pre-building a topological map where images as nodes and the number of traversing steps as edges for topological planning and localization. Then, the navigation system conducts image-goal conditioned travels in the scene while is robust against the localization lost issue. + +### Targeting users + +- Users that need a training pipeline of offline imitation learning or reinforcement learning that does not suffer from sim-to-real gap and can scale to handling larger datasets to achieve a generic AI agent similar to ViNT [3]. +- Users that need a visual navigation system in the deploying environments to implement image-goal navigation tasks such as last-mile delivering given an image as a goal. +- Users that need a non-metric map representation, i.e., an image-node topological graph for long-horizon planning and localization with low storing and updating cost. +- Developers that want to build a customized visual navigation systems for mobile or mobile manipulation tasks on the robots. + +## Design Details +### Architecture Design + + + +![framework](./images/framework.png "overall framework") +**Fig. 1. Overall framework.** (a) SCALE first pretrains a self-consistent representation space $z$ by the VAE-style loss, then fine-tunes it by the gradients from $Q(s, a, g)$ in the IQL. Next, we train the policy network $π$, temporal affordance model $ψ$ and the novelty predictor $f_ω$ over the trained representation space. (b) When the robot gets lost, SCALE randomly samples some transition $u$ from the prior $p(u)$ and feds it to the temporal affordance model to generate some multi-step latent trajectories recursively. Then it evaluates the candidates in terms of the reachability, anti-novelty and aggressiveness. Finally, it selects the optimal trajectory and executes the first step, then repeats until being successfully localized again. + + +The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned visual navigation policy and value function, 2) a neural topological SLAM module, and 3) a self-correcting module for autonomously recovering the localization. + +- **Image-goal conditioned visual navigation**: we use implicit Q-learning (IQL) [6], an offline reinforcement learning method to learn the value function and policy from offline dataset. In practice, we find two techniques useful for successfully learning the value functions from purely visual input: 1) *negative sampling* is necessary for value learning with paired image inputs, where we assign image pairs that are separated less than or equal to a threshold of timesteps $d_{max}$ as the positive samples $B_+$ and those separated beyond the threshold are assigned as the negative samples $B_−$. 2) *relative goal embedding*, the difference between goal and current image embeddings, *i.e.*, $∆z_{g,t} = z_g – z_t$, is more effective than directly using goal embedding $z_g$ as the input for the goalconditioned networks to perceive the goal orientation. +- **Self-correction for localization recovery**: we self-supervisedly learn an affordance model from offline dataset for generating potential future trajectories in the latent space. On the one hand, to predict multi-step trajectories, we use conditional generation model to learn the latent space with forward-inverse cycle consistency (FICC). On the other hand, we take advantage of the surrounding pixels in history frames using recurrent neural network (RNN) to predict some aggressive trajectories that are largely beyond the current field-of-view. Furthermore, we learn a novelty estimator via random network distillation (RND) [7] technique to evaluate the predicted future states. Intuitively, the reachability and aggressiveness facilitate the reasonability and diversity, while the anti-novelty strategy can induce the robot to the familiar places. +- **Neural topological SLAM**: we construct a topological map in the environment where each node is a previously observed image and each edge refers to the traversability between two nodes that is estimated by the learned value function. The localization process is conducted by searching the highest value prediction between current image and all the images on the map. An example of the integration of navigation, neural topological SLAM, and localization recovery module is illustrated in Fig.2. +
    +architecture +
    + + **Fig. 2. Topological navigation with localization recovery**. SCALE combines the topological visual navigation with a novel localization recovery module. + + + + From 14c3152b8918b1df12eafba546b8ec35d19dc505 Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Wed, 28 Aug 2024 12:15:35 +0800 Subject: [PATCH 26/64] Update sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md Co-authored-by: Tomoya Fujita Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index c497d50..f942dbc 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -34,8 +34,9 @@ For developers and end users, the goals of this project are: ![framework](./images/framework.png "overall framework") -**Fig. 1. Overall framework.** (a) SCALE first pretrains a self-consistent representation space $z$ by the VAE-style loss, then fine-tunes it by the gradients from $Q(s, a, g)$ in the IQL. Next, we train the policy network $π$, temporal affordance model $ψ$ and the novelty predictor $f_ω$ over the trained representation space. (b) When the robot gets lost, SCALE randomly samples some transition $u$ from the prior $p(u)$ and feds it to the temporal affordance model to generate some multi-step latent trajectories recursively. Then it evaluates the candidates in terms of the reachability, anti-novelty and aggressiveness. Finally, it selects the optimal trajectory and executes the first step, then repeats until being successfully localized again. - +**Fig. 1. Overall framework.** +(a) SCALE first pretrains a self-consistent representation space $z$ by the VAE-style loss, then fine-tunes it by the gradients from $Q(s, a, g)$ in the IQL. Next, we train the policy network $π$, temporal affordance model $ψ$ and the novelty predictor $f_ω$ over the trained representation space. +(b) When the robot gets lost, SCALE randomly samples some transition $u$ from the prior $p(u)$ and feds it to the temporal affordance model to generate some multi-step latent trajectories recursively. Then it evaluates the candidates in terms of the reachability, anti-novelty and aggressiveness. Finally, it selects the optimal trajectory and executes the first step, then repeats until being successfully localized again. The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned visual navigation policy and value function, 2) a neural topological SLAM module, and 3) a self-correcting module for autonomously recovering the localization. From 66c3b180450f352285d62242d0f8038764ee17f6 Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Wed, 28 Aug 2024 12:16:47 +0800 Subject: [PATCH 27/64] Apply suggestions from code review Co-authored-by: Tomoya Fujita Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map.md | 8 -------- 1 file changed, 8 deletions(-) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index f942dbc..a0f3733 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -31,8 +31,6 @@ For developers and end users, the goals of this project are: ## Design Details ### Architecture Design - - ![framework](./images/framework.png "overall framework") **Fig. 1. Overall framework.** (a) SCALE first pretrains a self-consistent representation space $z$ by the VAE-style loss, then fine-tunes it by the gradients from $Q(s, a, g)$ in the IQL. Next, we train the policy network $π$, temporal affordance model $ψ$ and the novelty predictor $f_ω$ over the trained representation space. @@ -54,15 +52,9 @@ The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned ![topological_navigation](./images/topological_navigation.png "topological_navigation" )--> - - - - - ### Data Collection Module We manually teleoperate the mobile robot to collect the trajectory datasets in the deployed scenarios. Commonly, a totally 30-minute dataset in each scenario is enough for training the navigation policy. In each trajectory of the dataset, the data consists of an onboard RGB image $o_t$ and the estimated pose $p_t = (x, y, θ)$ from odometry at each timestep. Note that the estimated pose in offline dataset is only used to calculate the robot action for offline training, while we only use RGB image as the observation during inference. - ## References [1] D. Shah et al., “ViNG: Learning Open-World Navigation with Visual Goals”, 2021. [2] D. Shah et al., “Offline Reinforcement Learning for Visual Navigation”, 2022. From 7de5788e9573bab53716408bbbf8c8350ea06f2a Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Thu, 29 Aug 2024 13:11:03 +0800 Subject: [PATCH 28/64] Update sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md Co-authored-by: Tomoya Fujita Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map.md | 1 + 1 file changed, 1 insertion(+) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index a0f3733..c73e3aa 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -17,6 +17,7 @@ For developers and end users, the goals of this project are: ## Proposal ### Project scope + - This project considers the image-goal visual navigation task in both indoor and outdoor scenarios, which **inputs a RGB image and a specified goal image** and **outputs the actions (linear and angular velocities)** of the mobile robot at timestep t without LIDAR, GPS or IMU information. Its **downstream tasks** can include park delivery, mobile manipulation, or offline training a generic agent. - The method first collects some trajectories of the scene for pre-building a topological map where images as nodes and the number of traversing steps as edges for topological planning and localization. Then, the navigation system conducts image-goal conditioned travels in the scene while is robust against the localization lost issue. From c34b655c7dfed26819ed1b9fca59c4b4d87df591 Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Thu, 29 Aug 2024 13:11:23 +0800 Subject: [PATCH 29/64] Update sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md Co-authored-by: Tomoya Fujita Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map.md | 1 + 1 file changed, 1 insertion(+) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index c73e3aa..55e4a53 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -1,6 +1,7 @@ # Offline Visual Navigation without Metric Map ## Background + Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. Through real-world testing, we observe that due to the limited state distribution in the offline dataset, when facing the **out-of-distribution (OOD)** observations induced by illumination and scenario changes and the accumulative errors, the localization loss issue often arises that the robot fails to localize itself on a given map. In this case, the robot may get stuck or make infeasible decision, which significantly decreases the navigation success rates and even results in collision. And this often requires tedious human monitoring and correction. To address this issue, our work poses the question and takes one step forward:’ can we leverage the prior knowledge from offline data to guide the robot to **self-correct** the trajectory autonomously without human intervention or specific data collection?’ To this end, our key idea is learning from offline dataset to predict whether future trajectories lie in the prior distribution, and plan the future trajectories that can drive the robot to the familiar places autonomously. Real-world experiments show our method achieves +26% average success rates and x2 longer the average distance until intervention than the baselines (ViNG and ReViND), proving that our method can significantly reduce the human intervention for practical applications. This project, called "*SCALE: Self-Correcting Visual Navigation for Mobile Robots via From 0ae2314e1cddfdb11a8e83c6b50c5ce77779bda2 Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Thu, 29 Aug 2024 13:11:29 +0800 Subject: [PATCH 30/64] Update sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md Co-authored-by: Tomoya Fujita Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index 55e4a53..a77b45a 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -2,7 +2,7 @@ ## Background -Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. +Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route has been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. Through real-world testing, we observe that due to the limited state distribution in the offline dataset, when facing the **out-of-distribution (OOD)** observations induced by illumination and scenario changes and the accumulative errors, the localization loss issue often arises that the robot fails to localize itself on a given map. In this case, the robot may get stuck or make infeasible decision, which significantly decreases the navigation success rates and even results in collision. And this often requires tedious human monitoring and correction. To address this issue, our work poses the question and takes one step forward:’ can we leverage the prior knowledge from offline data to guide the robot to **self-correct** the trajectory autonomously without human intervention or specific data collection?’ To this end, our key idea is learning from offline dataset to predict whether future trajectories lie in the prior distribution, and plan the future trajectories that can drive the robot to the familiar places autonomously. Real-world experiments show our method achieves +26% average success rates and x2 longer the average distance until intervention than the baselines (ViNG and ReViND), proving that our method can significantly reduce the human intervention for practical applications. This project, called "*SCALE: Self-Correcting Visual Navigation for Mobile Robots via Anti-Novelty Estimation*", has been accepted by 2024 ICRA, see paper: https://arxiv.org/abs/2404.10675. From c77b056118435ce4393cf84ad1403297b024c9db Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Thu, 29 Aug 2024 13:11:37 +0800 Subject: [PATCH 31/64] Update sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md Co-authored-by: Tomoya Fujita Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map.md | 1 + 1 file changed, 1 insertion(+) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index a77b45a..c77a0d5 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -55,6 +55,7 @@ The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned ![topological_navigation](./images/topological_navigation.png "topological_navigation" )--> ### Data Collection Module + We manually teleoperate the mobile robot to collect the trajectory datasets in the deployed scenarios. Commonly, a totally 30-minute dataset in each scenario is enough for training the navigation policy. In each trajectory of the dataset, the data consists of an onboard RGB image $o_t$ and the estimated pose $p_t = (x, y, θ)$ from odometry at each timestep. Note that the estimated pose in offline dataset is only used to calculate the robot action for offline training, while we only use RGB image as the observation during inference. ## References From 043ef74d599470941caa64f197a1df6ecb0ad783 Mon Sep 17 00:00:00 2001 From: chch9907 <799783944@qq.com> Date: Thu, 29 Aug 2024 18:14:05 +0800 Subject: [PATCH 32/64] modify the proposal Signed-off-by: chch9907 <799783944@qq.com> --- ...ne Visual Navigation without Metric Map.md | 52 +++++++++---------- 1 file changed, 25 insertions(+), 27 deletions(-) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index c77a0d5..1cceeeb 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -10,51 +10,49 @@ Anti-Novelty Estimation*", has been accepted by 2024 ICRA, see paper: https://ar ## Goals For developers and end users, the goals of this project are: -- Building a navigation system with only visual input and without metric maps. The policy is trained by directly learning from real-world offline datasets that does not suffer from any sim-to-real gap. Furthermore, the system is robust to the OOD observations induced by illumination and scenario changes and accumulative error issues during navigation. -- Building a low-cost system without metric map by using purely vision input for supporting neural topological SLAM, which particularly can be easily integrated into an instruction-followed navigation tasks by grounding the languages into the visual semantics similar to LM-Nav [5]. -- Addressing the localization loss issue by the proposed self-correcting method so as to enable long-term navigation with little human intervention during the process. -- Providing an offline dataset collection method to enable the users to finetune on the new environments. +- Building a navigation system with only visual input and without metric maps. The policy is trained by directly learning from real-world offline datasets that does not suffer from any sim-to-real gap. +- Enabling the system to be low-cost by using purely vision input for supporting neural topological SLAM, and can be easily integrated into an instruction-followed navigation tasks by grounding the languages on the visual semantics similar to LM-Nav [5]. +- Enabling the system to be robust to the OOD observations induced by illumination and scenario changes and accumulative error issues using the proposed self-correcting method so as to achieve long-horizon navigation with little human intervention during the process. +- Providing an offline dataset collection method to enable the users to finetune on the new environments. Meanwhile, curating a large real-world trajectory dataset collected from diverse end devices in different scenarios, with different illuminations and seasons, and on different robots, which is absent in the current open-source datasets and is important to facilitate the models to be more generalized and robust. -## Proposal - -### Project scope +## Proposals -- This project considers the image-goal visual navigation task in both indoor and outdoor scenarios, which **inputs a RGB image and a specified goal image** and **outputs the actions (linear and angular velocities)** of the mobile robot at timestep t without LIDAR, GPS or IMU information. Its **downstream tasks** can include park delivery, mobile manipulation, or offline training a generic agent. +### Project Scope -- The method first collects some trajectories of the scene for pre-building a topological map where images as nodes and the number of traversing steps as edges for topological planning and localization. Then, the navigation system conducts image-goal conditioned travels in the scene while is robust against the localization lost issue. +- This project considers the image-goal visual navigation task in both indoor and outdoor scenarios, which **inputs a RGB image and a specified goal image** and **outputs the actions (linear and angular velocities)** of the mobile robots at timestep t without LIDAR, GPS or IMU information. Its **downstream tasks** can include park delivery, mobile manipulation, or offline training a generic agent. +- This project provides **both training and deployment pipelines** for using our method in the users' scenarios, while trained models can be used either on cloud or on edge since they are not too large for real-time inference. +- The method requires first collecting some trajectories of the scene for pre-building a topological map where images as nodes and the number of traversing steps as edges for topological planning and localization. Then, the navigation models are trained on the collected trajectoris, after which the complete system can conduct image-goal conditioned travels in the scene while is robust to the localization loss issue. -### Targeting users +### Targeting Users - Users that need a training pipeline of offline imitation learning or reinforcement learning that does not suffer from sim-to-real gap and can scale to handling larger datasets to achieve a generic AI agent similar to ViNT [3]. -- Users that need a visual navigation system in the deploying environments to implement image-goal navigation tasks such as last-mile delivering given an image as a goal. +- Users that need a visual navigation system in the deploying environments to implement image-goal navigation tasks such as park delivering given an image as a goal. - Users that need a non-metric map representation, i.e., an image-node topological graph for long-horizon planning and localization with low storing and updating cost. -- Developers that want to build a customized visual navigation systems for mobile or mobile manipulation tasks on the robots. +- Developers that want to build a customized visual navigation system for mobile or mobile manipulation tasks according to their preference. ## Design Details + ### Architecture Design ![framework](./images/framework.png "overall framework") -**Fig. 1. Overall framework.** +**Fig. 1. Overall pipelines for offline training and online inference.** (a) SCALE first pretrains a self-consistent representation space $z$ by the VAE-style loss, then fine-tunes it by the gradients from $Q(s, a, g)$ in the IQL. Next, we train the policy network $π$, temporal affordance model $ψ$ and the novelty predictor $f_ω$ over the trained representation space. -(b) When the robot gets lost, SCALE randomly samples some transition $u$ from the prior $p(u)$ and feds it to the temporal affordance model to generate some multi-step latent trajectories recursively. Then it evaluates the candidates in terms of the reachability, anti-novelty and aggressiveness. Finally, it selects the optimal trajectory and executes the first step, then repeats until being successfully localized again. +(b) During inference, when the robot gets lost, SCALE randomly samples some transition $u$ from the prior $p(u)$ and feds it to the temporal affordance model to generate some multi-step latent trajectories recursively. Then it evaluates the candidates in terms of the reachability, anti-novelty and aggressiveness. Finally, it selects the optimal trajectory and executes the first step, then repeats until being successfully localized again. -The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned visual navigation policy and value function, 2) a neural topological SLAM module, and 3) a self-correcting module for autonomously recovering the localization. +The complete navigation system consists of 1) an image-goal conditioned visual navigation policy and value function, 2) a self-correcting module for autonomously recovering the localization and 3) a neural topological SLAM module, where the pipelines of the first two terms are illustrated in Fig. 1. -- **Image-goal conditioned visual navigation**: we use implicit Q-learning (IQL) [6], an offline reinforcement learning method to learn the value function and policy from offline dataset. In practice, we find two techniques useful for successfully learning the value functions from purely visual input: 1) *negative sampling* is necessary for value learning with paired image inputs, where we assign image pairs that are separated less than or equal to a threshold of timesteps $d_{max}$ as the positive samples $B_+$ and those separated beyond the threshold are assigned as the negative samples $B_−$. 2) *relative goal embedding*, the difference between goal and current image embeddings, *i.e.*, $∆z_{g,t} = z_g – z_t$, is more effective than directly using goal embedding $z_g$ as the input for the goalconditioned networks to perceive the goal orientation. -- **Self-correction for localization recovery**: we self-supervisedly learn an affordance model from offline dataset for generating potential future trajectories in the latent space. On the one hand, to predict multi-step trajectories, we use conditional generation model to learn the latent space with forward-inverse cycle consistency (FICC). On the other hand, we take advantage of the surrounding pixels in history frames using recurrent neural network (RNN) to predict some aggressive trajectories that are largely beyond the current field-of-view. Furthermore, we learn a novelty estimator via random network distillation (RND) [7] technique to evaluate the predicted future states. Intuitively, the reachability and aggressiveness facilitate the reasonability and diversity, while the anti-novelty strategy can induce the robot to the familiar places. -- **Neural topological SLAM**: we construct a topological map in the environment where each node is a previously observed image and each edge refers to the traversability between two nodes that is estimated by the learned value function. The localization process is conducted by searching the highest value prediction between current image and all the images on the map. An example of the integration of navigation, neural topological SLAM, and localization recovery module is illustrated in Fig.2. -
    -architecture +- **Image-goal conditioned visual navigation**: we use implicit Q-learning (IQL) [6], an offline reinforcement learning method to learn the value function and policy from offline dataset. In practice, we find two techniques useful for successfully learning the value functions from purely visual input: 1) *Negative sampling* is necessary for value learning with paired image inputs, where we assign image pairs that are separated less than or equal to a threshold of timesteps $d_{max}$ as the positive samples $B_+$ and those separated beyond the threshold are assigned as the negative samples $B_−$. 2) *Relative goal embedding*, the difference between goal and current image embeddings, *i.e.*, $∆z_{g,t} = z_g – z_t$, is more effective than directly using goal embedding $z_g$ as the input for the goalconditioned networks to perceive the goal orientation. +- **Self-correction for localization recovery**: we self-supervisedly learn an affordance model from offline dataset for generating potential future trajectories in the latent space. On the one hand, to predict multi-step trajectories, we use conditional generation model to learn the latent space with forward-inverse cycle consistency (FICC). On the other hand, we take advantage of the surrounding pixels in history frames using recurrent neural network (RNN) to predict some aggressive trajectories that are largely beyond the current field-of-view. Furthermore, we learn a novelty estimator via random network distillation (RND) [7] technique to evaluate the predicted future states. Intuitively, the reachability and aggressiveness facilitate the reasonability and diversity, while the anti-novelty strategy can induce the robot to the familiar places. +- **Neural topological SLAM**: we construct a topological map in the environment where each node is a previously observed image and each edge refers to the traversability between two nodes that is estimated by the learned value function. The localization process is conducted by searching the highest value prediction between current image and all the images on the map. + +An example of the integration of visual navigation, neural topological SLAM, and localization recovery modules is illustrated in Fig.2. +
    + architecture
    +**Fig. 2. Visual navigation with neural topological SLAM and localization recovery**. SCALE combines the topological visual navigation with a novel localization recovery module. We first build a topological map (gray cycles and lines) based on the offline dataset. Next, starting at the yellow cycle, we use the localization module to do active initialization. Then, given a goal image, we search a route (orange cycles) on the topological graph and execute to the goal (red cycle) step by step. - **Fig. 2. Topological navigation with localization recovery**. SCALE combines the topological visual navigation with a novel localization recovery module. - - -### Data Collection Module +### Data Collection We manually teleoperate the mobile robot to collect the trajectory datasets in the deployed scenarios. Commonly, a totally 30-minute dataset in each scenario is enough for training the navigation policy. In each trajectory of the dataset, the data consists of an onboard RGB image $o_t$ and the estimated pose $p_t = (x, y, θ)$ from odometry at each timestep. Note that the estimated pose in offline dataset is only used to calculate the robot action for offline training, while we only use RGB image as the observation during inference. From bb9f770bc6d80ed311f68a6d9c500bcbb5df7f89 Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Sat, 24 Aug 2024 12:41:57 +0800 Subject: [PATCH 33/64] Create Offline Visual Navigation without Metric Map.md add scale proposal Signed-off-by: chch9907 <799783944@qq.com> --- ...ne Visual Navigation without Metric Map.md | 146 +----------------- 1 file changed, 1 insertion(+), 145 deletions(-) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index 1cceeeb..3201cd8 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -63,148 +63,4 @@ We manually teleoperate the mobile robot to collect the trajectory datasets in t [4] D.S. Chaplot et al., “Neural Topological SLAM for Visual Navigation”, 2020 [5] D. Shah et al., “LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action”, 2022 [6] Kostrikov et al., “Offline Reinforcement Learning with Implicit Q-Learning”, 2021. -[7] Burda et al., “Exploration by Random Network Distillation”, 2018. -# Offline Visual Navigation without Metric Map - -## Background -Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. - -Through real-world testing, we observe that due to the limited state distribution in the offline dataset, when facing the **out-of-distribution (OOD)** observations induced by illumination and scenario changes and the accumulative errors, the localization loss issue often arises that the robot fails to localize itself on a given map. In this case, the robot may get stuck or make infeasible decision, which significantly decreases the navigation success rates and even results in collision. And this often requires tedious human monitoring and correction. To address this issue, our work poses the question and takes one step forward:’ can we leverage the prior knowledge from offline data to guide the robot to **self-correct** the trajectory autonomously without human intervention or specific data collection?’ To this end, our key idea is learning from offline dataset to predict whether future trajectories lie in the prior distribution, and plan the future trajectories that can drive the robot to the familiar places autonomously. Real-world experiments show our method achieves +26% average success rates and x2 longer the average distance until intervention than the baselines (ViNG and ReViND), proving that our method can significantly reduce the human intervention for practical applications. This project, called "*SCALE: Self-Correcting Visual Navigation for Mobile Robots via -Anti-Novelty Estimation*", has been accepted by 2024 ICRA, see paper: https://arxiv.org/abs/2404.10675. - -## Goals - -For developers and end users, the goals of this project are: -- Building a navigation system with only visual input and without metric maps. The policy is trained by directly learning from real-world offline datasets that does not suffer from any sim-to-real gap. Furthermore, the system is robust to the OOD observations induced by illumination and scenario changes and accumulative error issues during navigation. -- Building a low-cost system without metric map by using purely vision input for supporting neural topological SLAM, which particularly can be easily integrated into an instruction-followed navigation tasks by grounding the languages into the visual semantics similar to LM-Nav [5]. -- Addressing the localization loss issue by the proposed self-correcting method so as to enable long-term navigation with little human intervention during the process. -- Providing an offline dataset collection method to enable the users to finetune on the new environments. - -## Proposal - -### Project scope -- This project considers the image-goal visual navigation task in both indoor and outdoor scenarios, which **inputs a RGB image and a specified goal image** and **outputs the actions (linear and angular velocities)** of the mobile robot at timestep t without LIDAR, GPS or IMU information. Its **downstream tasks** can include park delivery, mobile manipulation, or offline training a generic agent. - -- The method first collects some trajectories of the scene for pre-building a topological map where images as nodes and the number of traversing steps as edges for topological planning and localization. Then, the navigation system conducts image-goal conditioned travels in the scene while is robust against the localization lost issue. - -### Targeting users - -- Users that need a training pipeline of offline imitation learning or reinforcement learning that does not suffer from sim-to-real gap and can scale to handling larger datasets to achieve a generic AI agent similar to ViNT [3]. -- Users that need a visual navigation system in the deploying environments to implement image-goal navigation tasks such as last-mile delivering given an image as a goal. -- Users that need a non-metric map representation, i.e., an image-node topological graph for long-horizon planning and localization with low storing and updating cost. -- Developers that want to build a customized visual navigation systems for mobile or mobile manipulation tasks on the robots. - -## Design Details -### Architecture Design - - - -![framework](./images/framework.png "overall framework") -**Fig. 1. Overall framework.** (a) SCALE first pretrains a self-consistent representation space $z$ by the VAE-style loss, then fine-tunes it by the gradients from $Q(s, a, g)$ in the IQL. Next, we train the policy network $π$, temporal affordance model $ψ$ and the novelty predictor $f_ω$ over the trained representation space. (b) When the robot gets lost, SCALE randomly samples some transition $u$ from the prior $p(u)$ and feds it to the temporal affordance model to generate some multi-step latent trajectories recursively. Then it evaluates the candidates in terms of the reachability, anti-novelty and aggressiveness. Finally, it selects the optimal trajectory and executes the first step, then repeats until being successfully localized again. - - -The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned visual navigation policy and value function, 2) a neural topological SLAM module, and 3) a self-correcting module for autonomously recovering the localization. - -- **Image-goal conditioned visual navigation**: we use implicit Q-learning (IQL) [6], an offline reinforcement learning method to learn the value function and policy from offline dataset. In practice, we find two techniques useful for successfully learning the value functions from purely visual input: 1) *negative sampling* is necessary for value learning with paired image inputs, where we assign image pairs that are separated less than or equal to a threshold of timesteps $d_{max}$ as the positive samples $B_+$ and those separated beyond the threshold are assigned as the negative samples $B_−$. 2) *relative goal embedding*, the difference between goal and current image embeddings, *i.e.*, $∆z_{g,t} = z_g – z_t$, is more effective than directly using goal embedding $z_g$ as the input for the goalconditioned networks to perceive the goal orientation. -- **Self-correction for localization recovery**: we self-supervisedly learn an affordance model from offline dataset for generating potential future trajectories in the latent space. On the one hand, to predict multi-step trajectories, we use conditional generation model to learn the latent space with forward-inverse cycle consistency (FICC). On the other hand, we take advantage of the surrounding pixels in history frames using recurrent neural network (RNN) to predict some aggressive trajectories that are largely beyond the current field-of-view. Furthermore, we learn a novelty estimator via random network distillation (RND) [7] technique to evaluate the predicted future states. Intuitively, the reachability and aggressiveness facilitate the reasonability and diversity, while the anti-novelty strategy can induce the robot to the familiar places. -- **Neural topological SLAM**: we construct a topological map in the environment where each node is a previously observed image and each edge refers to the traversability between two nodes that is estimated by the learned value function. The localization process is conducted by searching the highest value prediction between current image and all the images on the map. An example of the integration of navigation, neural topological SLAM, and localization recovery module is illustrated in Fig.2. -
    -architecture -
    - - **Fig. 2. Topological navigation with localization recovery**. SCALE combines the topological visual navigation with a novel localization recovery module. - - - - - - - - -### Data Collection Module -We manually teleoperate the mobile robot to collect the trajectory datasets in the deployed scenarios. Commonly, a totally 30-minute dataset in each scenario is enough for training the navigation policy. In each trajectory of the dataset, the data consists of an onboard RGB image $o_t$ and the estimated pose $p_t = (x, y, θ)$ from odometry at each timestep. Note that the estimated pose in offline dataset is only used to calculate the robot action for offline training, while we only use RGB image as the observation during inference. - - -## References -[1] D. Shah et al., “ViNG: Learning Open-World Navigation with Visual Goals”, 2021. -[2] D. Shah et al., “Offline Reinforcement Learning for Visual Navigation”, 2022. -[3] D. Shah et al., “ViNT: A Foundation Model for Visual Navigation”, 2023. -[4] D.S. Chaplot et al., “Neural Topological SLAM for Visual Navigation”, 2020 -[5] D. Shah et al., “LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action”, 2022 -[6] Kostrikov et al., “Offline Reinforcement Learning with Implicit Q-Learning”, 2021. -[7] Burda et al., “Exploration by Random Network Distillation”, 2018. -# Offline Visual Navigation without Metric Map - -## Background -Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. - -Through real-world testing, we observe that due to the limited state distribution in the offline dataset, when facing the **out-of-distribution (OOD)** observations induced by illumination and scenario changes and the accumulative errors, the localization loss issue often arises that the robot fails to localize itself on a given map. In this case, the robot may get stuck or make infeasible decision, which significantly decreases the navigation success rates and even results in collision. And this often requires tedious human monitoring and correction. To address this issue, our work poses the question and takes one step forward:’ can we leverage the prior knowledge from offline data to guide the robot to **self-correct** the trajectory autonomously without human intervention or specific data collection?’ To this end, our key idea is learning from offline dataset to predict whether future trajectories lie in the prior distribution, and plan the future trajectories that can drive the robot to the familiar places autonomously. Real-world experiments show our method achieves +26% average success rates and x2 longer the average distance until intervention than the baselines (ViNG and ReViND), proving that our method can significantly reduce the human intervention for practical applications. This project, called "*SCALE: Self-Correcting Visual Navigation for Mobile Robots via -Anti-Novelty Estimation*", has been accepted by 2024 ICRA, see paper: https://arxiv.org/abs/2404.10675. - -## Goals - -For developers and end users, the goals of this project are: -- Building a navigation system with only visual input and without metric maps. The policy is trained by directly learning from real-world offline datasets that does not suffer from any sim-to-real gap. Furthermore, the system is robust to the OOD observations induced by illumination and scenario changes and accumulative error issues during navigation. -- Building a low-cost system without metric map by using purely vision input for supporting neural topological SLAM, which particularly can be easily integrated into an instruction-followed navigation tasks by grounding the languages into the visual semantics similar to LM-Nav [5]. -- Addressing the localization loss issue by the proposed self-correcting method so as to enable long-term navigation with little human intervention during the process. -- Providing an offline dataset collection method to enable the users to finetune on the new environments. - -## Proposal - -### Project scope -- This project considers the image-goal visual navigation task in both indoor and outdoor scenarios, which **inputs a RGB image and a specified goal image** and **outputs the actions (linear and angular velocities)** of the mobile robot at timestep t without LIDAR, GPS or IMU information. Its **downstream tasks** can include park delivery, mobile manipulation, or offline training a generic agent. - -- The method first collects some trajectories of the scene for pre-building a topological map where images as nodes and the number of traversing steps as edges for topological planning and localization. Then, the navigation system conducts image-goal conditioned travels in the scene while is robust against the localization lost issue. - -### Targeting users - -- Users that need a training pipeline of offline imitation learning or reinforcement learning that does not suffer from sim-to-real gap and can scale to handling larger datasets to achieve a generic AI agent similar to ViNT [3]. -- Users that need a visual navigation system in the deploying environments to implement image-goal navigation tasks such as last-mile delivering given an image as a goal. -- Users that need a non-metric map representation, i.e., an image-node topological graph for long-horizon planning and localization with low storing and updating cost. -- Developers that want to build a customized visual navigation systems for mobile or mobile manipulation tasks on the robots. - -## Design Details -### Architecture Design - - - -![framework](./images/framework.png "overall framework") -**Fig. 1. Overall framework.** (a) SCALE first pretrains a self-consistent representation space $z$ by the VAE-style loss, then fine-tunes it by the gradients from $Q(s, a, g)$ in the IQL. Next, we train the policy network $π$, temporal affordance model $ψ$ and the novelty predictor $f_ω$ over the trained representation space. (b) When the robot gets lost, SCALE randomly samples some transition $u$ from the prior $p(u)$ and feds it to the temporal affordance model to generate some multi-step latent trajectories recursively. Then it evaluates the candidates in terms of the reachability, anti-novelty and aggressiveness. Finally, it selects the optimal trajectory and executes the first step, then repeats until being successfully localized again. - - -The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned visual navigation policy and value function, 2) a neural topological SLAM module, and 3) a self-correcting module for autonomously recovering the localization. - -- **Image-goal conditioned visual navigation**: we use implicit Q-learning (IQL) [6], an offline reinforcement learning method to learn the value function and policy from offline dataset. In practice, we find two techniques useful for successfully learning the value functions from purely visual input: 1) *negative sampling* is necessary for value learning with paired image inputs, where we assign image pairs that are separated less than or equal to a threshold of timesteps $d_{max}$ as the positive samples $B_+$ and those separated beyond the threshold are assigned as the negative samples $B_−$. 2) *relative goal embedding*, the difference between goal and current image embeddings, *i.e.*, $∆z_{g,t} = z_g – z_t$, is more effective than directly using goal embedding $z_g$ as the input for the goalconditioned networks to perceive the goal orientation. -- **Self-correction for localization recovery**: we self-supervisedly learn an affordance model from offline dataset for generating potential future trajectories in the latent space. On the one hand, to predict multi-step trajectories, we use conditional generation model to learn the latent space with forward-inverse cycle consistency (FICC). On the other hand, we take advantage of the surrounding pixels in history frames using recurrent neural network (RNN) to predict some aggressive trajectories that are largely beyond the current field-of-view. Furthermore, we learn a novelty estimator via random network distillation (RND) [7] technique to evaluate the predicted future states. Intuitively, the reachability and aggressiveness facilitate the reasonability and diversity, while the anti-novelty strategy can induce the robot to the familiar places. -- **Neural topological SLAM**: we construct a topological map in the environment where each node is a previously observed image and each edge refers to the traversability between two nodes that is estimated by the learned value function. The localization process is conducted by searching the highest value prediction between current image and all the images on the map. An example of the integration of navigation, neural topological SLAM, and localization recovery module is illustrated in Fig.2. -
    -architecture -
    - - **Fig. 2. Topological navigation with localization recovery**. SCALE combines the topological visual navigation with a novel localization recovery module. - - - - - - - - -### Data Collection Module -We manually teleoperate the mobile robot to collect the trajectory datasets in the deployed scenarios. Commonly, a totally 30-minute dataset in each scenario is enough for training the navigation policy. In each trajectory of the dataset, the data consists of an onboard RGB image $o_t$ and the estimated pose $p_t = (x, y, θ)$ from odometry at each timestep. Note that the estimated pose in offline dataset is only used to calculate the robot action for offline training, while we only use RGB image as the observation during inference. - - -## References -[1] D. Shah et al., “ViNG: Learning Open-World Navigation with Visual Goals”, 2021. -[2] D. Shah et al., “Offline Reinforcement Learning for Visual Navigation”, 2022. -[3] D. Shah et al., “ViNT: A Foundation Model for Visual Navigation”, 2023. -[4] D.S. Chaplot et al., “Neural Topological SLAM for Visual Navigation”, 2020 -[5] D. Shah et al., “LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action”, 2022 -[6] Kostrikov et al., “Offline Reinforcement Learning with Implicit Q-Learning”, 2021. -[7] Burda et al., “Exploration by Random Network Distillation”, 2018. +[7] Burda et al., “Exploration by Random Network Distillation”, 2018. \ No newline at end of file From 1bdeceaff1db6eaeb8fc1457a63bd24ca6fac33a Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Sat, 24 Aug 2024 13:08:10 +0800 Subject: [PATCH 34/64] add Offline Visual Navigation without Metric Map Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map/images/test.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt new file mode 100644 index 0000000..e8689b2 --- /dev/null +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt @@ -0,0 +1 @@ +hh From 5c90c46d6da8524b6936b5a3ad98bdc4daa72044 Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Sat, 24 Aug 2024 13:09:35 +0800 Subject: [PATCH 35/64] Delete sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map/images/test.txt | 1 - 1 file changed, 1 deletion(-) delete mode 100644 sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt deleted file mode 100644 index e8689b2..0000000 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt +++ /dev/null @@ -1 +0,0 @@ -hh From 0e394a6ff22f4cf0a643862425cbee8de52a47c8 Mon Sep 17 00:00:00 2001 From: chch9907 <799783944@qq.com> Date: Thu, 29 Aug 2024 21:59:43 +0800 Subject: [PATCH 36/64] modify the proposal Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map.md | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index 3201cd8..06bf293 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -10,9 +10,9 @@ Anti-Novelty Estimation*", has been accepted by 2024 ICRA, see paper: https://ar ## Goals For developers and end users, the goals of this project are: -- Building a navigation system with only visual input and without metric maps. The policy is trained by directly learning from real-world offline datasets that does not suffer from any sim-to-real gap. -- Enabling the system to be low-cost by using purely vision input for supporting neural topological SLAM, and can be easily integrated into an instruction-followed navigation tasks by grounding the languages on the visual semantics similar to LM-Nav [5]. -- Enabling the system to be robust to the OOD observations induced by illumination and scenario changes and accumulative error issues using the proposed self-correcting method so as to achieve long-horizon navigation with little human intervention during the process. +- Building a image-goal conditioned visual navigation system with only visual input and without metric maps. The models are trained by directly learning from real-world offline datasets that does not suffer from any sim-to-real gap. +- The system can be easily integrated into an instruction-followed navigation tasks by grounding the languages on the visual semantics similar to LM-Nav [5]. +- The system can be robust to the OOD observations induced by illumination and scenario changes and accumulative error issues using the proposed self-correcting method so as to achieve long-horizon navigation with little human intervention during the process. - Providing an offline dataset collection method to enable the users to finetune on the new environments. Meanwhile, curating a large real-world trajectory dataset collected from diverse end devices in different scenarios, with different illuminations and seasons, and on different robots, which is absent in the current open-source datasets and is important to facilitate the models to be more generalized and robust. ## Proposals @@ -45,11 +45,12 @@ The complete navigation system consists of 1) an image-goal conditioned visual n - **Self-correction for localization recovery**: we self-supervisedly learn an affordance model from offline dataset for generating potential future trajectories in the latent space. On the one hand, to predict multi-step trajectories, we use conditional generation model to learn the latent space with forward-inverse cycle consistency (FICC). On the other hand, we take advantage of the surrounding pixels in history frames using recurrent neural network (RNN) to predict some aggressive trajectories that are largely beyond the current field-of-view. Furthermore, we learn a novelty estimator via random network distillation (RND) [7] technique to evaluate the predicted future states. Intuitively, the reachability and aggressiveness facilitate the reasonability and diversity, while the anti-novelty strategy can induce the robot to the familiar places. - **Neural topological SLAM**: we construct a topological map in the environment where each node is a previously observed image and each edge refers to the traversability between two nodes that is estimated by the learned value function. The localization process is conducted by searching the highest value prediction between current image and all the images on the map. -An example of the integration of visual navigation, neural topological SLAM, and localization recovery modules is illustrated in Fig.2. +An example of the integration of visual navigation, neural topological SLAM, and localization recovery modules is illustrated in Fig.2. Our method combines the topological visual navigation with a novel localization recovery module. We first build a topological map (gray cycles and lines) based on the offline dataset. Next, starting at the yellow cycle, we use the localization module to do active initialization. Then, given a goal image, we search a route (orange cycles) on the topological graph and execute to the goal (red cycle) step by step.
    architecture
    -**Fig. 2. Visual navigation with neural topological SLAM and localization recovery**. SCALE combines the topological visual navigation with a novel localization recovery module. We first build a topological map (gray cycles and lines) based on the offline dataset. Next, starting at the yellow cycle, we use the localization module to do active initialization. Then, given a goal image, we search a route (orange cycles) on the topological graph and execute to the goal (red cycle) step by step. + +**Fig. 2. Visual navigation with neural topological SLAM and localization recovery**. ### Data Collection From be6f4f86a5bba01b78459fded478d07c234c6804 Mon Sep 17 00:00:00 2001 From: chch9907 <799783944@qq.com> Date: Wed, 11 Sep 2024 15:50:57 +0800 Subject: [PATCH 37/64] modify proposal Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index 06bf293..bbf16cb 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -5,12 +5,13 @@ Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route has been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. Through real-world testing, we observe that due to the limited state distribution in the offline dataset, when facing the **out-of-distribution (OOD)** observations induced by illumination and scenario changes and the accumulative errors, the localization loss issue often arises that the robot fails to localize itself on a given map. In this case, the robot may get stuck or make infeasible decision, which significantly decreases the navigation success rates and even results in collision. And this often requires tedious human monitoring and correction. To address this issue, our work poses the question and takes one step forward:’ can we leverage the prior knowledge from offline data to guide the robot to **self-correct** the trajectory autonomously without human intervention or specific data collection?’ To this end, our key idea is learning from offline dataset to predict whether future trajectories lie in the prior distribution, and plan the future trajectories that can drive the robot to the familiar places autonomously. Real-world experiments show our method achieves +26% average success rates and x2 longer the average distance until intervention than the baselines (ViNG and ReViND), proving that our method can significantly reduce the human intervention for practical applications. This project, called "*SCALE: Self-Correcting Visual Navigation for Mobile Robots via -Anti-Novelty Estimation*", has been accepted by 2024 ICRA, see paper: https://arxiv.org/abs/2404.10675. +Anti-Novelty Estimation*", has been accepted by 2024 ICRA, see paper: https://ieeexplore.ieee.org/document/10610847. + ## Goals For developers and end users, the goals of this project are: -- Building a image-goal conditioned visual navigation system with only visual input and without metric maps. The models are trained by directly learning from real-world offline datasets that does not suffer from any sim-to-real gap. +- Building an image-goal conditioned visual navigation system with only visual input and without metric maps. The models are trained by directly learning from real-world offline datasets that does not suffer from any sim-to-real gap. - The system can be easily integrated into an instruction-followed navigation tasks by grounding the languages on the visual semantics similar to LM-Nav [5]. - The system can be robust to the OOD observations induced by illumination and scenario changes and accumulative error issues using the proposed self-correcting method so as to achieve long-horizon navigation with little human intervention during the process. - Providing an offline dataset collection method to enable the users to finetune on the new environments. Meanwhile, curating a large real-world trajectory dataset collected from diverse end devices in different scenarios, with different illuminations and seasons, and on different robots, which is absent in the current open-source datasets and is important to facilitate the models to be more generalized and robust. From 624eca9b29632aaa19423fea79b8af58539422a8 Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Sat, 24 Aug 2024 12:41:57 +0800 Subject: [PATCH 38/64] Create Offline Visual Navigation without Metric Map.md add scale proposal Signed-off-by: chch9907 <799783944@qq.com> --- ...ne Visual Navigation without Metric Map.md | 64 +++++++++++++++++++ 1 file changed, 64 insertions(+) create mode 100644 sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md new file mode 100644 index 0000000..e6e852b --- /dev/null +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -0,0 +1,64 @@ +# Offline Visual Navigation without Metric Map + +## Background +Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. + +Through real-world testing, we observe that due to the limited state distribution in the offline dataset, when facing the **out-of-distribution (OOD)** observations induced by illumination and scenario changes and the accumulative errors, the localization loss issue often arises that the robot fails to localize itself on a given map. In this case, the robot may get stuck or make infeasible decision, which significantly decreases the navigation success rates and even results in collision. And this often requires tedious human monitoring and correction. To address this issue, our work poses the question and takes one step forward:’ can we leverage the prior knowledge from offline data to guide the robot to **self-correct** the trajectory autonomously without human intervention or specific data collection?’ To this end, our key idea is learning from offline dataset to predict whether future trajectories lie in the prior distribution, and plan the future trajectories that can drive the robot to the familiar places autonomously. Real-world experiments show our method achieves +26% average success rates and x2 longer the average distance until intervention than the baselines (ViNG and ReViND), proving that our method can significantly reduce the human intervention for practical applications. This project has been accepted by 2024 ICRA, see paper: https://arxiv.org/abs/2404.10675. + +## Goals + +For developers and end users, the goals of this project are: +- Building a navigation system with only visual input and without metric maps. The policy is trained by directly learning from real-world offline datasets that does not suffer from any sim-to-real gap. Furthermore, the system is robust to the OOD observations induced by illumination and scenario changes and accumulative error issues during navigation. +- Building a low-cost system without metric map by using purely vision input for supporting neural topological SLAM, which particularly can be easily integrated into an instruction-followed navigation tasks by grounding the languages into the visual semantics similar to LM-Nav [5]. +- Addressing the localization loss issue by the proposed self-correcting method so as to enable long-term navigation with little human intervention during the process. +- Providing an offline dataset collection method to enable the users to finetune on the new environments. + +## Proposal + +### Project scope +- This project considers the image-goal visual navigation task in both indoor and outdoor scenarios, which **inputs a RGB image and a specified goal image** and **outputs the actions (linear and angular velocities)** of the mobile robot at timestep t without LIDAR, GPS or IMU information. Its **downstream tasks** can include park delivery, mobile manipulation, or offline training a generic agent. + +- The method first collects some trajectories of the scene for pre-building a topological map where images as nodes and the number of traversing steps as edges for topological planning and localization. Then, the navigation system conducts image-goal conditioned travels in the scene while is robust against the localization lost issue. + +### Targeting users + +- Users that need a training pipeline of offline imitation learning or reinforcement learning that does not suffer from sim-to-real gap and can scale to handling larger datasets to achieve a generic AI agent similar to ViNT [3]. +- Users that need a visual navigation system in the deploying environments to implement image-goal navigation tasks such as last-mile delivering given an image as a goal. +- Users that need a non-metric map representation, i.e., an image-node topological graph for long-horizon planning and localization with low storing and updating cost. +- Developers that want to build a customized visual navigation systems for mobile or mobile manipulation tasks on the robots. + +## Design Details +### Architecture Design + + + +![framework](./images/framework.png "overall framework") +**Fig. 1. Overall framework.** (a) SCALE first pretrains a self-consistent representation space $z$ by the VAE-style loss, then fine-tunes it by the gradients from $Q(s, a, g)$ in the IQL. Next, we train the policy network $π$, temporal affordance model $ψ$ and the novelty predictor $f_ω$ over the trained representation space. (b) When the robot gets lost, SCALE randomly samples some transition $u$ from the prior $p(u)$ and feds it to the temporal affordance model to generate some multi-step latent trajectories recursively. Then it evaluates the candidates in terms of the reachability, anti-novelty and aggressiveness. Finally, it selects the optimal trajectory and executes the first step, then repeats until being successfully localized again. + + +The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned visual navigation policy and value function, 2) a neural topological SLAM module, and 3) a self-correcting module for autonomously recovering the localization. + +- **Image-goal conditioned visual navigation**: we use implicit Q-learning (IQL) [6], an offline reinforcement learning method to learn the value function and policy from offline dataset. In practice, we find two techniques useful for successfully learning the value functions from purely visual input: 1) *negative sampling* is necessary for value learning with paired image inputs, where we assign image pairs that are separated less than or equal to a threshold of timesteps $d_{max}$ as the positive samples $B_+$ and those separated beyond the threshold are assigned as the negative samples $B_−$. 2) *relative goal embedding*, the difference between goal and current image embeddings, *i.e.*, $∆z_{g,t} = z_g – z_t$, is more effective than directly using goal embedding $z_g$ as the input for the goalconditioned networks to perceive the goal orientation. +- **Self-correction for localization recovery**: we self-supervisedly learn an affordance model from offline dataset for generating potential future trajectories in the latent space. On the one hand, to predict multi-step trajectories, we use conditional generation model to learn the latent space with forward-inverse cycle consistency (FICC). On the other hand, we take advantage of the surrounding pixels in history frames using recurrent neural network (RNN) to predict some aggressive trajectories that are largely beyond the current field-of-view. Furthermore, we learn a novelty estimator via random network distillation (RND) [7] technique to evaluate the predicted future states. Intuitively, the reachability and aggressiveness facilitate the reasonability and diversity, while the anti-novelty strategy can induce the robot to the familiar places. +- **Neural topological SLAM**: we construct a topological map in the environment where each node is a previously observed image and each edge refers to the traversability between two nodes that is estimated by the learned value function. The localization process is conducted by searching the highest value prediction between current image and all the images on the map. An example of the integration of navigation, neural topological SLAM, and localization recovery module is illustrated in Fig.2. + ![topological_navigation](./images/topological_navigation.png "topological_navigation" ) + **Fig. 2. Topological navigation with localization recovery**. SCALE combines the topological visual navigation with a novel localization recovery module. + + + + + +### Data Collection Module +We manually teleoperate the mobile robot to collect the trajectory datasets in the deployed scenarios. Commonly, a totally 30-minute dataset in each scenario is enough for training the navigation policy. In each trajectory of the dataset, the data consists of an onboard RGB image $o_t$ and the estimated pose $p_t = (x, y, θ)$ from odometry at each timestep. Note that the estimated pose in offline dataset is only used to calculate the robot action for offline training, while we only use RGB image as the observation during inference. + + +## References +[1] D. Shah et al., “ViNG: Learning Open-World Navigation with Visual Goals”, 2021. +[2] D. Shah et al., “Offline Reinforcement Learning for Visual Navigation”, 2022. +[3] D. Shah et al., “ViNT: A Foundation Model for Visual Navigation”, 2023. +[4] D.S. Chaplot et al., “Neural Topological SLAM for Visual Navigation”, 2020 +[5] D. Shah et al., “LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action”, 2022 +[6] Kostrikov et al., “Offline Reinforcement Learning with Implicit Q-Learning”, 2021. +[7] Burda et al., “Exploration by Random Network Distillation”, 2018. From ca5cff295a87182b6eeb1d1bb527c998a5e595b9 Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Sat, 24 Aug 2024 13:08:10 +0800 Subject: [PATCH 39/64] add Offline Visual Navigation without Metric Map Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map/images/test.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt new file mode 100644 index 0000000..e8689b2 --- /dev/null +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt @@ -0,0 +1 @@ +hh From 349eb529f8cffbe62eab78c44a5630515f7157bc Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Sat, 24 Aug 2024 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      n@lYx96vXV*?4Pqvt{|C}P BWRU;> literal 0 HcmV?d00001 From c8215677703a388c17ea86d3504f1d9a036b833f Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Sat, 24 Aug 2024 13:09:35 +0800 Subject: [PATCH 41/64] Delete sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map/images/test.txt | 1 - 1 file changed, 1 deletion(-) delete mode 100644 sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt deleted file mode 100644 index e8689b2..0000000 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt +++ /dev/null @@ -1 +0,0 @@ -hh From c7fbfa14ecc4d5ac63ab3dcc5191ec244b84ca5c Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Sat, 24 Aug 2024 13:16:53 +0800 Subject: [PATCH 42/64] Update Offline Visual Navigation without Metric Map.md Signed-off-by: chch9907 <799783944@qq.com> --- ...ine Visual Navigation without Metric Map.md | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index e6e852b..2f39346 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -3,7 +3,8 @@ ## Background Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. -Through real-world testing, we observe that due to the limited state distribution in the offline dataset, when facing the **out-of-distribution (OOD)** observations induced by illumination and scenario changes and the accumulative errors, the localization loss issue often arises that the robot fails to localize itself on a given map. In this case, the robot may get stuck or make infeasible decision, which significantly decreases the navigation success rates and even results in collision. And this often requires tedious human monitoring and correction. To address this issue, our work poses the question and takes one step forward:’ can we leverage the prior knowledge from offline data to guide the robot to **self-correct** the trajectory autonomously without human intervention or specific data collection?’ To this end, our key idea is learning from offline dataset to predict whether future trajectories lie in the prior distribution, and plan the future trajectories that can drive the robot to the familiar places autonomously. Real-world experiments show our method achieves +26% average success rates and x2 longer the average distance until intervention than the baselines (ViNG and ReViND), proving that our method can significantly reduce the human intervention for practical applications. This project has been accepted by 2024 ICRA, see paper: https://arxiv.org/abs/2404.10675. +Through real-world testing, we observe that due to the limited state distribution in the offline dataset, when facing the **out-of-distribution (OOD)** observations induced by illumination and scenario changes and the accumulative errors, the localization loss issue often arises that the robot fails to localize itself on a given map. In this case, the robot may get stuck or make infeasible decision, which significantly decreases the navigation success rates and even results in collision. And this often requires tedious human monitoring and correction. To address this issue, our work poses the question and takes one step forward:’ can we leverage the prior knowledge from offline data to guide the robot to **self-correct** the trajectory autonomously without human intervention or specific data collection?’ To this end, our key idea is learning from offline dataset to predict whether future trajectories lie in the prior distribution, and plan the future trajectories that can drive the robot to the familiar places autonomously. Real-world experiments show our method achieves +26% average success rates and x2 longer the average distance until intervention than the baselines (ViNG and ReViND), proving that our method can significantly reduce the human intervention for practical applications. This project, called "*SCALE: Self-Correcting Visual Navigation for Mobile Robots via +Anti-Novelty Estimation*", has been accepted by 2024 ICRA, see paper: https://arxiv.org/abs/2404.10675. ## Goals @@ -41,11 +42,18 @@ The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned - **Image-goal conditioned visual navigation**: we use implicit Q-learning (IQL) [6], an offline reinforcement learning method to learn the value function and policy from offline dataset. In practice, we find two techniques useful for successfully learning the value functions from purely visual input: 1) *negative sampling* is necessary for value learning with paired image inputs, where we assign image pairs that are separated less than or equal to a threshold of timesteps $d_{max}$ as the positive samples $B_+$ and those separated beyond the threshold are assigned as the negative samples $B_−$. 2) *relative goal embedding*, the difference between goal and current image embeddings, *i.e.*, $∆z_{g,t} = z_g – z_t$, is more effective than directly using goal embedding $z_g$ as the input for the goalconditioned networks to perceive the goal orientation. - **Self-correction for localization recovery**: we self-supervisedly learn an affordance model from offline dataset for generating potential future trajectories in the latent space. On the one hand, to predict multi-step trajectories, we use conditional generation model to learn the latent space with forward-inverse cycle consistency (FICC). On the other hand, we take advantage of the surrounding pixels in history frames using recurrent neural network (RNN) to predict some aggressive trajectories that are largely beyond the current field-of-view. Furthermore, we learn a novelty estimator via random network distillation (RND) [7] technique to evaluate the predicted future states. Intuitively, the reachability and aggressiveness facilitate the reasonability and diversity, while the anti-novelty strategy can induce the robot to the familiar places. - **Neural topological SLAM**: we construct a topological map in the environment where each node is a previously observed image and each edge refers to the traversability between two nodes that is estimated by the learned value function. The localization process is conducted by searching the highest value prediction between current image and all the images on the map. An example of the integration of navigation, neural topological SLAM, and localization recovery module is illustrated in Fig.2. - ![topological_navigation](./images/topological_navigation.png "topological_navigation" ) +

      + **Fig. 2. Topological navigation with localization recovery**. SCALE combines the topological visual navigation with a novel localization recovery module. - + + + + From f1f34568f5b8218e0ad098b8049e78a977508d14 Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Wed, 28 Aug 2024 12:15:35 +0800 Subject: [PATCH 43/64] Update sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md Co-authored-by: Tomoya Fujita Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index 2f39346..a020a34 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -34,8 +34,9 @@ For developers and end users, the goals of this project are: ![framework](./images/framework.png "overall framework") -**Fig. 1. Overall framework.** (a) SCALE first pretrains a self-consistent representation space $z$ by the VAE-style loss, then fine-tunes it by the gradients from $Q(s, a, g)$ in the IQL. Next, we train the policy network $π$, temporal affordance model $ψ$ and the novelty predictor $f_ω$ over the trained representation space. (b) When the robot gets lost, SCALE randomly samples some transition $u$ from the prior $p(u)$ and feds it to the temporal affordance model to generate some multi-step latent trajectories recursively. Then it evaluates the candidates in terms of the reachability, anti-novelty and aggressiveness. Finally, it selects the optimal trajectory and executes the first step, then repeats until being successfully localized again. - +**Fig. 1. Overall framework.** +(a) SCALE first pretrains a self-consistent representation space $z$ by the VAE-style loss, then fine-tunes it by the gradients from $Q(s, a, g)$ in the IQL. Next, we train the policy network $π$, temporal affordance model $ψ$ and the novelty predictor $f_ω$ over the trained representation space. +(b) When the robot gets lost, SCALE randomly samples some transition $u$ from the prior $p(u)$ and feds it to the temporal affordance model to generate some multi-step latent trajectories recursively. Then it evaluates the candidates in terms of the reachability, anti-novelty and aggressiveness. Finally, it selects the optimal trajectory and executes the first step, then repeats until being successfully localized again. The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned visual navigation policy and value function, 2) a neural topological SLAM module, and 3) a self-correcting module for autonomously recovering the localization. From 1ec562812c2eaf598730d8914b3b07171b09e03a Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Wed, 28 Aug 2024 12:16:47 +0800 Subject: [PATCH 44/64] Apply suggestions from code review Co-authored-by: Tomoya Fujita Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map.md | 8 -------- 1 file changed, 8 deletions(-) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index a020a34..8802085 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -31,8 +31,6 @@ For developers and end users, the goals of this project are: ## Design Details ### Architecture Design - - ![framework](./images/framework.png "overall framework") **Fig. 1. Overall framework.** (a) SCALE first pretrains a self-consistent representation space $z$ by the VAE-style loss, then fine-tunes it by the gradients from $Q(s, a, g)$ in the IQL. Next, we train the policy network $π$, temporal affordance model $ψ$ and the novelty predictor $f_ω$ over the trained representation space. @@ -54,15 +52,9 @@ The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned ![topological_navigation](./images/topological_navigation.png "topological_navigation" )--> - - - - - ### Data Collection Module We manually teleoperate the mobile robot to collect the trajectory datasets in the deployed scenarios. Commonly, a totally 30-minute dataset in each scenario is enough for training the navigation policy. In each trajectory of the dataset, the data consists of an onboard RGB image $o_t$ and the estimated pose $p_t = (x, y, θ)$ from odometry at each timestep. Note that the estimated pose in offline dataset is only used to calculate the robot action for offline training, while we only use RGB image as the observation during inference. - ## References [1] D. Shah et al., “ViNG: Learning Open-World Navigation with Visual Goals”, 2021. [2] D. Shah et al., “Offline Reinforcement Learning for Visual Navigation”, 2022. From a7d44303c63319cc3f79d89460cee6b6cca3897a Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Thu, 29 Aug 2024 13:11:03 +0800 Subject: [PATCH 45/64] Update sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md Co-authored-by: Tomoya Fujita Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map.md | 1 + 1 file changed, 1 insertion(+) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index 8802085..4af6e96 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -17,6 +17,7 @@ For developers and end users, the goals of this project are: ## Proposal ### Project scope + - This project considers the image-goal visual navigation task in both indoor and outdoor scenarios, which **inputs a RGB image and a specified goal image** and **outputs the actions (linear and angular velocities)** of the mobile robot at timestep t without LIDAR, GPS or IMU information. Its **downstream tasks** can include park delivery, mobile manipulation, or offline training a generic agent. - The method first collects some trajectories of the scene for pre-building a topological map where images as nodes and the number of traversing steps as edges for topological planning and localization. Then, the navigation system conducts image-goal conditioned travels in the scene while is robust against the localization lost issue. From a84dca1b0dcbba6d1d9f18a81e30184ea9f12376 Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Thu, 29 Aug 2024 13:11:23 +0800 Subject: [PATCH 46/64] Update sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md Co-authored-by: Tomoya Fujita Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map.md | 1 + 1 file changed, 1 insertion(+) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index 4af6e96..e2f5dba 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -1,6 +1,7 @@ # Offline Visual Navigation without Metric Map ## Background + Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. Through real-world testing, we observe that due to the limited state distribution in the offline dataset, when facing the **out-of-distribution (OOD)** observations induced by illumination and scenario changes and the accumulative errors, the localization loss issue often arises that the robot fails to localize itself on a given map. In this case, the robot may get stuck or make infeasible decision, which significantly decreases the navigation success rates and even results in collision. And this often requires tedious human monitoring and correction. To address this issue, our work poses the question and takes one step forward:’ can we leverage the prior knowledge from offline data to guide the robot to **self-correct** the trajectory autonomously without human intervention or specific data collection?’ To this end, our key idea is learning from offline dataset to predict whether future trajectories lie in the prior distribution, and plan the future trajectories that can drive the robot to the familiar places autonomously. Real-world experiments show our method achieves +26% average success rates and x2 longer the average distance until intervention than the baselines (ViNG and ReViND), proving that our method can significantly reduce the human intervention for practical applications. This project, called "*SCALE: Self-Correcting Visual Navigation for Mobile Robots via From 76cd245f5744774f658bf5151c8dac80430f8851 Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Thu, 29 Aug 2024 13:11:29 +0800 Subject: [PATCH 47/64] Update sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md Co-authored-by: Tomoya Fujita Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index e2f5dba..0307d0d 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -2,7 +2,7 @@ ## Background -Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. +Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route has been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. Through real-world testing, we observe that due to the limited state distribution in the offline dataset, when facing the **out-of-distribution (OOD)** observations induced by illumination and scenario changes and the accumulative errors, the localization loss issue often arises that the robot fails to localize itself on a given map. In this case, the robot may get stuck or make infeasible decision, which significantly decreases the navigation success rates and even results in collision. And this often requires tedious human monitoring and correction. To address this issue, our work poses the question and takes one step forward:’ can we leverage the prior knowledge from offline data to guide the robot to **self-correct** the trajectory autonomously without human intervention or specific data collection?’ To this end, our key idea is learning from offline dataset to predict whether future trajectories lie in the prior distribution, and plan the future trajectories that can drive the robot to the familiar places autonomously. Real-world experiments show our method achieves +26% average success rates and x2 longer the average distance until intervention than the baselines (ViNG and ReViND), proving that our method can significantly reduce the human intervention for practical applications. This project, called "*SCALE: Self-Correcting Visual Navigation for Mobile Robots via Anti-Novelty Estimation*", has been accepted by 2024 ICRA, see paper: https://arxiv.org/abs/2404.10675. From b7f166b2d3e3b1760f03fdc2609da3cc5f446e55 Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Thu, 29 Aug 2024 13:11:37 +0800 Subject: [PATCH 48/64] Update sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md Co-authored-by: Tomoya Fujita Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map.md | 1 + 1 file changed, 1 insertion(+) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index 0307d0d..8b1d7c3 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -55,6 +55,7 @@ The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned ![topological_navigation](./images/topological_navigation.png "topological_navigation" )--> ### Data Collection Module + We manually teleoperate the mobile robot to collect the trajectory datasets in the deployed scenarios. Commonly, a totally 30-minute dataset in each scenario is enough for training the navigation policy. In each trajectory of the dataset, the data consists of an onboard RGB image $o_t$ and the estimated pose $p_t = (x, y, θ)$ from odometry at each timestep. Note that the estimated pose in offline dataset is only used to calculate the robot action for offline training, while we only use RGB image as the observation during inference. ## References From 9fe94a029017113e0098a84e21c56db0e51e813b Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Sat, 24 Aug 2024 12:41:57 +0800 Subject: [PATCH 49/64] Create Offline Visual Navigation without Metric Map.md add scale proposal Signed-off-by: chch9907 <799783944@qq.com> --- ...ne Visual Navigation without Metric Map.md | 80 +++++++++++++++++-- 1 file changed, 74 insertions(+), 6 deletions(-) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index 8b1d7c3..cba3863 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -1,8 +1,7 @@ # Offline Visual Navigation without Metric Map ## Background - -Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route has been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. +Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. Through real-world testing, we observe that due to the limited state distribution in the offline dataset, when facing the **out-of-distribution (OOD)** observations induced by illumination and scenario changes and the accumulative errors, the localization loss issue often arises that the robot fails to localize itself on a given map. In this case, the robot may get stuck or make infeasible decision, which significantly decreases the navigation success rates and even results in collision. And this often requires tedious human monitoring and correction. To address this issue, our work poses the question and takes one step forward:’ can we leverage the prior knowledge from offline data to guide the robot to **self-correct** the trajectory autonomously without human intervention or specific data collection?’ To this end, our key idea is learning from offline dataset to predict whether future trajectories lie in the prior distribution, and plan the future trajectories that can drive the robot to the familiar places autonomously. Real-world experiments show our method achieves +26% average success rates and x2 longer the average distance until intervention than the baselines (ViNG and ReViND), proving that our method can significantly reduce the human intervention for practical applications. This project, called "*SCALE: Self-Correcting Visual Navigation for Mobile Robots via Anti-Novelty Estimation*", has been accepted by 2024 ICRA, see paper: https://arxiv.org/abs/2404.10675. @@ -18,7 +17,6 @@ For developers and end users, the goals of this project are: ## Proposal ### Project scope - - This project considers the image-goal visual navigation task in both indoor and outdoor scenarios, which **inputs a RGB image and a specified goal image** and **outputs the actions (linear and angular velocities)** of the mobile robot at timestep t without LIDAR, GPS or IMU information. Its **downstream tasks** can include park delivery, mobile manipulation, or offline training a generic agent. - The method first collects some trajectories of the scene for pre-building a topological map where images as nodes and the number of traversing steps as edges for topological planning and localization. Then, the navigation system conducts image-goal conditioned travels in the scene while is robust against the localization lost issue. @@ -33,10 +31,11 @@ For developers and end users, the goals of this project are: ## Design Details ### Architecture Design + + ![framework](./images/framework.png "overall framework") -**Fig. 1. Overall framework.** -(a) SCALE first pretrains a self-consistent representation space $z$ by the VAE-style loss, then fine-tunes it by the gradients from $Q(s, a, g)$ in the IQL. Next, we train the policy network $π$, temporal affordance model $ψ$ and the novelty predictor $f_ω$ over the trained representation space. -(b) When the robot gets lost, SCALE randomly samples some transition $u$ from the prior $p(u)$ and feds it to the temporal affordance model to generate some multi-step latent trajectories recursively. Then it evaluates the candidates in terms of the reachability, anti-novelty and aggressiveness. Finally, it selects the optimal trajectory and executes the first step, then repeats until being successfully localized again. +**Fig. 1. Overall framework.** (a) SCALE first pretrains a self-consistent representation space $z$ by the VAE-style loss, then fine-tunes it by the gradients from $Q(s, a, g)$ in the IQL. Next, we train the policy network $π$, temporal affordance model $ψ$ and the novelty predictor $f_ω$ over the trained representation space. (b) When the robot gets lost, SCALE randomly samples some transition $u$ from the prior $p(u)$ and feds it to the temporal affordance model to generate some multi-step latent trajectories recursively. Then it evaluates the candidates in terms of the reachability, anti-novelty and aggressiveness. Finally, it selects the optimal trajectory and executes the first step, then repeats until being successfully localized again. + The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned visual navigation policy and value function, 2) a neural topological SLAM module, and 3) a self-correcting module for autonomously recovering the localization. @@ -54,10 +53,79 @@ The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned ![topological_navigation](./images/topological_navigation.png "topological_navigation" )--> + + + + + ### Data Collection Module +We manually teleoperate the mobile robot to collect the trajectory datasets in the deployed scenarios. Commonly, a totally 30-minute dataset in each scenario is enough for training the navigation policy. In each trajectory of the dataset, the data consists of an onboard RGB image $o_t$ and the estimated pose $p_t = (x, y, θ)$ from odometry at each timestep. Note that the estimated pose in offline dataset is only used to calculate the robot action for offline training, while we only use RGB image as the observation during inference. + + +## References +[1] D. Shah et al., “ViNG: Learning Open-World Navigation with Visual Goals”, 2021. +[2] D. Shah et al., “Offline Reinforcement Learning for Visual Navigation”, 2022. +[3] D. Shah et al., “ViNT: A Foundation Model for Visual Navigation”, 2023. +[4] D.S. Chaplot et al., “Neural Topological SLAM for Visual Navigation”, 2020 +[5] D. Shah et al., “LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action”, 2022 +[6] Kostrikov et al., “Offline Reinforcement Learning with Implicit Q-Learning”, 2021. +[7] Burda et al., “Exploration by Random Network Distillation”, 2018. +# Offline Visual Navigation without Metric Map + +## Background +Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. + +Through real-world testing, we observe that due to the limited state distribution in the offline dataset, when facing the **out-of-distribution (OOD)** observations induced by illumination and scenario changes and the accumulative errors, the localization loss issue often arises that the robot fails to localize itself on a given map. In this case, the robot may get stuck or make infeasible decision, which significantly decreases the navigation success rates and even results in collision. And this often requires tedious human monitoring and correction. To address this issue, our work poses the question and takes one step forward:’ can we leverage the prior knowledge from offline data to guide the robot to **self-correct** the trajectory autonomously without human intervention or specific data collection?’ To this end, our key idea is learning from offline dataset to predict whether future trajectories lie in the prior distribution, and plan the future trajectories that can drive the robot to the familiar places autonomously. Real-world experiments show our method achieves +26% average success rates and x2 longer the average distance until intervention than the baselines (ViNG and ReViND), proving that our method can significantly reduce the human intervention for practical applications. This project has been accepted by 2024 ICRA, see paper: https://arxiv.org/abs/2404.10675. + +## Goals + +For developers and end users, the goals of this project are: +- Building a navigation system with only visual input and without metric maps. The policy is trained by directly learning from real-world offline datasets that does not suffer from any sim-to-real gap. Furthermore, the system is robust to the OOD observations induced by illumination and scenario changes and accumulative error issues during navigation. +- Building a low-cost system without metric map by using purely vision input for supporting neural topological SLAM, which particularly can be easily integrated into an instruction-followed navigation tasks by grounding the languages into the visual semantics similar to LM-Nav [5]. +- Addressing the localization loss issue by the proposed self-correcting method so as to enable long-term navigation with little human intervention during the process. +- Providing an offline dataset collection method to enable the users to finetune on the new environments. + +## Proposal + +### Project scope +- This project considers the image-goal visual navigation task in both indoor and outdoor scenarios, which **inputs a RGB image and a specified goal image** and **outputs the actions (linear and angular velocities)** of the mobile robot at timestep t without LIDAR, GPS or IMU information. Its **downstream tasks** can include park delivery, mobile manipulation, or offline training a generic agent. + +- The method first collects some trajectories of the scene for pre-building a topological map where images as nodes and the number of traversing steps as edges for topological planning and localization. Then, the navigation system conducts image-goal conditioned travels in the scene while is robust against the localization lost issue. + +### Targeting users + +- Users that need a training pipeline of offline imitation learning or reinforcement learning that does not suffer from sim-to-real gap and can scale to handling larger datasets to achieve a generic AI agent similar to ViNT [3]. +- Users that need a visual navigation system in the deploying environments to implement image-goal navigation tasks such as last-mile delivering given an image as a goal. +- Users that need a non-metric map representation, i.e., an image-node topological graph for long-horizon planning and localization with low storing and updating cost. +- Developers that want to build a customized visual navigation systems for mobile or mobile manipulation tasks on the robots. + +## Design Details +### Architecture Design + + +![framework](./images/framework.png "overall framework") +**Fig. 1. Overall framework.** (a) SCALE first pretrains a self-consistent representation space $z$ by the VAE-style loss, then fine-tunes it by the gradients from $Q(s, a, g)$ in the IQL. Next, we train the policy network $π$, temporal affordance model $ψ$ and the novelty predictor $f_ω$ over the trained representation space. (b) When the robot gets lost, SCALE randomly samples some transition $u$ from the prior $p(u)$ and feds it to the temporal affordance model to generate some multi-step latent trajectories recursively. Then it evaluates the candidates in terms of the reachability, anti-novelty and aggressiveness. Finally, it selects the optimal trajectory and executes the first step, then repeats until being successfully localized again. + + +The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned visual navigation policy and value function, 2) a neural topological SLAM module, and 3) a self-correcting module for autonomously recovering the localization. + +- **Image-goal conditioned visual navigation**: we use implicit Q-learning (IQL) [6], an offline reinforcement learning method to learn the value function and policy from offline dataset. In practice, we find two techniques useful for successfully learning the value functions from purely visual input: 1) *negative sampling* is necessary for value learning with paired image inputs, where we assign image pairs that are separated less than or equal to a threshold of timesteps $d_{max}$ as the positive samples $B_+$ and those separated beyond the threshold are assigned as the negative samples $B_−$. 2) *relative goal embedding*, the difference between goal and current image embeddings, *i.e.*, $∆z_{g,t} = z_g – z_t$, is more effective than directly using goal embedding $z_g$ as the input for the goalconditioned networks to perceive the goal orientation. +- **Self-correction for localization recovery**: we self-supervisedly learn an affordance model from offline dataset for generating potential future trajectories in the latent space. On the one hand, to predict multi-step trajectories, we use conditional generation model to learn the latent space with forward-inverse cycle consistency (FICC). On the other hand, we take advantage of the surrounding pixels in history frames using recurrent neural network (RNN) to predict some aggressive trajectories that are largely beyond the current field-of-view. Furthermore, we learn a novelty estimator via random network distillation (RND) [7] technique to evaluate the predicted future states. Intuitively, the reachability and aggressiveness facilitate the reasonability and diversity, while the anti-novelty strategy can induce the robot to the familiar places. +- **Neural topological SLAM**: we construct a topological map in the environment where each node is a previously observed image and each edge refers to the traversability between two nodes that is estimated by the learned value function. The localization process is conducted by searching the highest value prediction between current image and all the images on the map. An example of the integration of navigation, neural topological SLAM, and localization recovery module is illustrated in Fig.2. + ![topological_navigation](./images/topological_navigation.png "topological_navigation" ) + **Fig. 2. Topological navigation with localization recovery**. SCALE combines the topological visual navigation with a novel localization recovery module. + + + + + +### Data Collection Module We manually teleoperate the mobile robot to collect the trajectory datasets in the deployed scenarios. Commonly, a totally 30-minute dataset in each scenario is enough for training the navigation policy. In each trajectory of the dataset, the data consists of an onboard RGB image $o_t$ and the estimated pose $p_t = (x, y, θ)$ from odometry at each timestep. Note that the estimated pose in offline dataset is only used to calculate the robot action for offline training, while we only use RGB image as the observation during inference. + ## References [1] D. Shah et al., “ViNG: Learning Open-World Navigation with Visual Goals”, 2021. [2] D. Shah et al., “Offline Reinforcement Learning for Visual Navigation”, 2022. From 2e8825be397a25215f4bb5565d942c176d7dbdd6 Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Sat, 24 Aug 2024 13:08:10 +0800 Subject: [PATCH 50/64] add Offline Visual Navigation without Metric Map Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map/images/test.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt new file mode 100644 index 0000000..e8689b2 --- /dev/null +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt @@ -0,0 +1 @@ +hh From 3dfe6eabcffee120450b777d891153d3c666696f Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Sat, 24 Aug 2024 13:09:35 +0800 Subject: [PATCH 51/64] Delete sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map/images/test.txt | 1 - 1 file changed, 1 deletion(-) delete mode 100644 sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt deleted file mode 100644 index e8689b2..0000000 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt +++ /dev/null @@ -1 +0,0 @@ -hh From 6bbb92c0b7c1d6e91316c846c0188e1e92b31fb6 Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Sat, 24 Aug 2024 13:16:53 +0800 Subject: [PATCH 52/64] Update Offline Visual Navigation without Metric Map.md Signed-off-by: chch9907 <799783944@qq.com> --- ...ne Visual Navigation without Metric Map.md | 90 +++++++++++++++++-- 1 file changed, 85 insertions(+), 5 deletions(-) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index cba3863..c497d50 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -75,7 +75,8 @@ We manually teleoperate the mobile robot to collect the trajectory datasets in t ## Background Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. -Through real-world testing, we observe that due to the limited state distribution in the offline dataset, when facing the **out-of-distribution (OOD)** observations induced by illumination and scenario changes and the accumulative errors, the localization loss issue often arises that the robot fails to localize itself on a given map. In this case, the robot may get stuck or make infeasible decision, which significantly decreases the navigation success rates and even results in collision. And this often requires tedious human monitoring and correction. To address this issue, our work poses the question and takes one step forward:’ can we leverage the prior knowledge from offline data to guide the robot to **self-correct** the trajectory autonomously without human intervention or specific data collection?’ To this end, our key idea is learning from offline dataset to predict whether future trajectories lie in the prior distribution, and plan the future trajectories that can drive the robot to the familiar places autonomously. Real-world experiments show our method achieves +26% average success rates and x2 longer the average distance until intervention than the baselines (ViNG and ReViND), proving that our method can significantly reduce the human intervention for practical applications. This project has been accepted by 2024 ICRA, see paper: https://arxiv.org/abs/2404.10675. +Through real-world testing, we observe that due to the limited state distribution in the offline dataset, when facing the **out-of-distribution (OOD)** observations induced by illumination and scenario changes and the accumulative errors, the localization loss issue often arises that the robot fails to localize itself on a given map. In this case, the robot may get stuck or make infeasible decision, which significantly decreases the navigation success rates and even results in collision. And this often requires tedious human monitoring and correction. To address this issue, our work poses the question and takes one step forward:’ can we leverage the prior knowledge from offline data to guide the robot to **self-correct** the trajectory autonomously without human intervention or specific data collection?’ To this end, our key idea is learning from offline dataset to predict whether future trajectories lie in the prior distribution, and plan the future trajectories that can drive the robot to the familiar places autonomously. Real-world experiments show our method achieves +26% average success rates and x2 longer the average distance until intervention than the baselines (ViNG and ReViND), proving that our method can significantly reduce the human intervention for practical applications. This project, called "*SCALE: Self-Correcting Visual Navigation for Mobile Robots via +Anti-Novelty Estimation*", has been accepted by 2024 ICRA, see paper: https://arxiv.org/abs/2404.10675. ## Goals @@ -113,11 +114,90 @@ The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned - **Image-goal conditioned visual navigation**: we use implicit Q-learning (IQL) [6], an offline reinforcement learning method to learn the value function and policy from offline dataset. In practice, we find two techniques useful for successfully learning the value functions from purely visual input: 1) *negative sampling* is necessary for value learning with paired image inputs, where we assign image pairs that are separated less than or equal to a threshold of timesteps $d_{max}$ as the positive samples $B_+$ and those separated beyond the threshold are assigned as the negative samples $B_−$. 2) *relative goal embedding*, the difference between goal and current image embeddings, *i.e.*, $∆z_{g,t} = z_g – z_t$, is more effective than directly using goal embedding $z_g$ as the input for the goalconditioned networks to perceive the goal orientation. - **Self-correction for localization recovery**: we self-supervisedly learn an affordance model from offline dataset for generating potential future trajectories in the latent space. On the one hand, to predict multi-step trajectories, we use conditional generation model to learn the latent space with forward-inverse cycle consistency (FICC). On the other hand, we take advantage of the surrounding pixels in history frames using recurrent neural network (RNN) to predict some aggressive trajectories that are largely beyond the current field-of-view. Furthermore, we learn a novelty estimator via random network distillation (RND) [7] technique to evaluate the predicted future states. Intuitively, the reachability and aggressiveness facilitate the reasonability and diversity, while the anti-novelty strategy can induce the robot to the familiar places. - **Neural topological SLAM**: we construct a topological map in the environment where each node is a previously observed image and each edge refers to the traversability between two nodes that is estimated by the learned value function. The localization process is conducted by searching the highest value prediction between current image and all the images on the map. An example of the integration of navigation, neural topological SLAM, and localization recovery module is illustrated in Fig.2. - ![topological_navigation](./images/topological_navigation.png "topological_navigation" ) +
      +architecture +
      + **Fig. 2. Topological navigation with localization recovery**. SCALE combines the topological visual navigation with a novel localization recovery module. - + + + + + + + + +### Data Collection Module +We manually teleoperate the mobile robot to collect the trajectory datasets in the deployed scenarios. Commonly, a totally 30-minute dataset in each scenario is enough for training the navigation policy. In each trajectory of the dataset, the data consists of an onboard RGB image $o_t$ and the estimated pose $p_t = (x, y, θ)$ from odometry at each timestep. Note that the estimated pose in offline dataset is only used to calculate the robot action for offline training, while we only use RGB image as the observation during inference. + + +## References +[1] D. Shah et al., “ViNG: Learning Open-World Navigation with Visual Goals”, 2021. +[2] D. Shah et al., “Offline Reinforcement Learning for Visual Navigation”, 2022. +[3] D. Shah et al., “ViNT: A Foundation Model for Visual Navigation”, 2023. +[4] D.S. Chaplot et al., “Neural Topological SLAM for Visual Navigation”, 2020 +[5] D. Shah et al., “LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action”, 2022 +[6] Kostrikov et al., “Offline Reinforcement Learning with Implicit Q-Learning”, 2021. +[7] Burda et al., “Exploration by Random Network Distillation”, 2018. +# Offline Visual Navigation without Metric Map + +## Background +Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. + +Through real-world testing, we observe that due to the limited state distribution in the offline dataset, when facing the **out-of-distribution (OOD)** observations induced by illumination and scenario changes and the accumulative errors, the localization loss issue often arises that the robot fails to localize itself on a given map. In this case, the robot may get stuck or make infeasible decision, which significantly decreases the navigation success rates and even results in collision. And this often requires tedious human monitoring and correction. To address this issue, our work poses the question and takes one step forward:’ can we leverage the prior knowledge from offline data to guide the robot to **self-correct** the trajectory autonomously without human intervention or specific data collection?’ To this end, our key idea is learning from offline dataset to predict whether future trajectories lie in the prior distribution, and plan the future trajectories that can drive the robot to the familiar places autonomously. Real-world experiments show our method achieves +26% average success rates and x2 longer the average distance until intervention than the baselines (ViNG and ReViND), proving that our method can significantly reduce the human intervention for practical applications. This project, called "*SCALE: Self-Correcting Visual Navigation for Mobile Robots via +Anti-Novelty Estimation*", has been accepted by 2024 ICRA, see paper: https://arxiv.org/abs/2404.10675. + +## Goals + +For developers and end users, the goals of this project are: +- Building a navigation system with only visual input and without metric maps. The policy is trained by directly learning from real-world offline datasets that does not suffer from any sim-to-real gap. Furthermore, the system is robust to the OOD observations induced by illumination and scenario changes and accumulative error issues during navigation. +- Building a low-cost system without metric map by using purely vision input for supporting neural topological SLAM, which particularly can be easily integrated into an instruction-followed navigation tasks by grounding the languages into the visual semantics similar to LM-Nav [5]. +- Addressing the localization loss issue by the proposed self-correcting method so as to enable long-term navigation with little human intervention during the process. +- Providing an offline dataset collection method to enable the users to finetune on the new environments. + +## Proposal + +### Project scope +- This project considers the image-goal visual navigation task in both indoor and outdoor scenarios, which **inputs a RGB image and a specified goal image** and **outputs the actions (linear and angular velocities)** of the mobile robot at timestep t without LIDAR, GPS or IMU information. Its **downstream tasks** can include park delivery, mobile manipulation, or offline training a generic agent. + +- The method first collects some trajectories of the scene for pre-building a topological map where images as nodes and the number of traversing steps as edges for topological planning and localization. Then, the navigation system conducts image-goal conditioned travels in the scene while is robust against the localization lost issue. + +### Targeting users + +- Users that need a training pipeline of offline imitation learning or reinforcement learning that does not suffer from sim-to-real gap and can scale to handling larger datasets to achieve a generic AI agent similar to ViNT [3]. +- Users that need a visual navigation system in the deploying environments to implement image-goal navigation tasks such as last-mile delivering given an image as a goal. +- Users that need a non-metric map representation, i.e., an image-node topological graph for long-horizon planning and localization with low storing and updating cost. +- Developers that want to build a customized visual navigation systems for mobile or mobile manipulation tasks on the robots. + +## Design Details +### Architecture Design + + + +![framework](./images/framework.png "overall framework") +**Fig. 1. Overall framework.** (a) SCALE first pretrains a self-consistent representation space $z$ by the VAE-style loss, then fine-tunes it by the gradients from $Q(s, a, g)$ in the IQL. Next, we train the policy network $π$, temporal affordance model $ψ$ and the novelty predictor $f_ω$ over the trained representation space. (b) When the robot gets lost, SCALE randomly samples some transition $u$ from the prior $p(u)$ and feds it to the temporal affordance model to generate some multi-step latent trajectories recursively. Then it evaluates the candidates in terms of the reachability, anti-novelty and aggressiveness. Finally, it selects the optimal trajectory and executes the first step, then repeats until being successfully localized again. + + +The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned visual navigation policy and value function, 2) a neural topological SLAM module, and 3) a self-correcting module for autonomously recovering the localization. + +- **Image-goal conditioned visual navigation**: we use implicit Q-learning (IQL) [6], an offline reinforcement learning method to learn the value function and policy from offline dataset. In practice, we find two techniques useful for successfully learning the value functions from purely visual input: 1) *negative sampling* is necessary for value learning with paired image inputs, where we assign image pairs that are separated less than or equal to a threshold of timesteps $d_{max}$ as the positive samples $B_+$ and those separated beyond the threshold are assigned as the negative samples $B_−$. 2) *relative goal embedding*, the difference between goal and current image embeddings, *i.e.*, $∆z_{g,t} = z_g – z_t$, is more effective than directly using goal embedding $z_g$ as the input for the goalconditioned networks to perceive the goal orientation. +- **Self-correction for localization recovery**: we self-supervisedly learn an affordance model from offline dataset for generating potential future trajectories in the latent space. On the one hand, to predict multi-step trajectories, we use conditional generation model to learn the latent space with forward-inverse cycle consistency (FICC). On the other hand, we take advantage of the surrounding pixels in history frames using recurrent neural network (RNN) to predict some aggressive trajectories that are largely beyond the current field-of-view. Furthermore, we learn a novelty estimator via random network distillation (RND) [7] technique to evaluate the predicted future states. Intuitively, the reachability and aggressiveness facilitate the reasonability and diversity, while the anti-novelty strategy can induce the robot to the familiar places. +- **Neural topological SLAM**: we construct a topological map in the environment where each node is a previously observed image and each edge refers to the traversability between two nodes that is estimated by the learned value function. The localization process is conducted by searching the highest value prediction between current image and all the images on the map. An example of the integration of navigation, neural topological SLAM, and localization recovery module is illustrated in Fig.2. +
      +architecture +
      + + **Fig. 2. Topological navigation with localization recovery**. SCALE combines the topological visual navigation with a novel localization recovery module. + + + + From c83598d0cafdb75f9b2bcb53b3d77a3e553f58e3 Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Wed, 28 Aug 2024 12:15:35 +0800 Subject: [PATCH 53/64] Update sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md Co-authored-by: Tomoya Fujita Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index c497d50..f942dbc 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -34,8 +34,9 @@ For developers and end users, the goals of this project are: ![framework](./images/framework.png "overall framework") -**Fig. 1. Overall framework.** (a) SCALE first pretrains a self-consistent representation space $z$ by the VAE-style loss, then fine-tunes it by the gradients from $Q(s, a, g)$ in the IQL. Next, we train the policy network $π$, temporal affordance model $ψ$ and the novelty predictor $f_ω$ over the trained representation space. (b) When the robot gets lost, SCALE randomly samples some transition $u$ from the prior $p(u)$ and feds it to the temporal affordance model to generate some multi-step latent trajectories recursively. Then it evaluates the candidates in terms of the reachability, anti-novelty and aggressiveness. Finally, it selects the optimal trajectory and executes the first step, then repeats until being successfully localized again. - +**Fig. 1. Overall framework.** +(a) SCALE first pretrains a self-consistent representation space $z$ by the VAE-style loss, then fine-tunes it by the gradients from $Q(s, a, g)$ in the IQL. Next, we train the policy network $π$, temporal affordance model $ψ$ and the novelty predictor $f_ω$ over the trained representation space. +(b) When the robot gets lost, SCALE randomly samples some transition $u$ from the prior $p(u)$ and feds it to the temporal affordance model to generate some multi-step latent trajectories recursively. Then it evaluates the candidates in terms of the reachability, anti-novelty and aggressiveness. Finally, it selects the optimal trajectory and executes the first step, then repeats until being successfully localized again. The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned visual navigation policy and value function, 2) a neural topological SLAM module, and 3) a self-correcting module for autonomously recovering the localization. From 4e8612fc7e0c9536815565e974ce1fa0e784a345 Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Wed, 28 Aug 2024 12:16:47 +0800 Subject: [PATCH 54/64] Apply suggestions from code review Co-authored-by: Tomoya Fujita Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map.md | 8 -------- 1 file changed, 8 deletions(-) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index f942dbc..a0f3733 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -31,8 +31,6 @@ For developers and end users, the goals of this project are: ## Design Details ### Architecture Design - - ![framework](./images/framework.png "overall framework") **Fig. 1. Overall framework.** (a) SCALE first pretrains a self-consistent representation space $z$ by the VAE-style loss, then fine-tunes it by the gradients from $Q(s, a, g)$ in the IQL. Next, we train the policy network $π$, temporal affordance model $ψ$ and the novelty predictor $f_ω$ over the trained representation space. @@ -54,15 +52,9 @@ The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned ![topological_navigation](./images/topological_navigation.png "topological_navigation" )--> - - - - - ### Data Collection Module We manually teleoperate the mobile robot to collect the trajectory datasets in the deployed scenarios. Commonly, a totally 30-minute dataset in each scenario is enough for training the navigation policy. In each trajectory of the dataset, the data consists of an onboard RGB image $o_t$ and the estimated pose $p_t = (x, y, θ)$ from odometry at each timestep. Note that the estimated pose in offline dataset is only used to calculate the robot action for offline training, while we only use RGB image as the observation during inference. - ## References [1] D. Shah et al., “ViNG: Learning Open-World Navigation with Visual Goals”, 2021. [2] D. Shah et al., “Offline Reinforcement Learning for Visual Navigation”, 2022. From 6dd501ce06e3812bc5f55dadea2659562dd7f38a Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Thu, 29 Aug 2024 13:11:03 +0800 Subject: [PATCH 55/64] Update sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md Co-authored-by: Tomoya Fujita Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map.md | 1 + 1 file changed, 1 insertion(+) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index a0f3733..c73e3aa 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -17,6 +17,7 @@ For developers and end users, the goals of this project are: ## Proposal ### Project scope + - This project considers the image-goal visual navigation task in both indoor and outdoor scenarios, which **inputs a RGB image and a specified goal image** and **outputs the actions (linear and angular velocities)** of the mobile robot at timestep t without LIDAR, GPS or IMU information. Its **downstream tasks** can include park delivery, mobile manipulation, or offline training a generic agent. - The method first collects some trajectories of the scene for pre-building a topological map where images as nodes and the number of traversing steps as edges for topological planning and localization. Then, the navigation system conducts image-goal conditioned travels in the scene while is robust against the localization lost issue. From 7dcf3d0bf7ae9565f279ce0741ee2da95b31d145 Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Thu, 29 Aug 2024 13:11:23 +0800 Subject: [PATCH 56/64] Update sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md Co-authored-by: Tomoya Fujita Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map.md | 1 + 1 file changed, 1 insertion(+) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index c73e3aa..55e4a53 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -1,6 +1,7 @@ # Offline Visual Navigation without Metric Map ## Background + Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. Through real-world testing, we observe that due to the limited state distribution in the offline dataset, when facing the **out-of-distribution (OOD)** observations induced by illumination and scenario changes and the accumulative errors, the localization loss issue often arises that the robot fails to localize itself on a given map. In this case, the robot may get stuck or make infeasible decision, which significantly decreases the navigation success rates and even results in collision. And this often requires tedious human monitoring and correction. To address this issue, our work poses the question and takes one step forward:’ can we leverage the prior knowledge from offline data to guide the robot to **self-correct** the trajectory autonomously without human intervention or specific data collection?’ To this end, our key idea is learning from offline dataset to predict whether future trajectories lie in the prior distribution, and plan the future trajectories that can drive the robot to the familiar places autonomously. Real-world experiments show our method achieves +26% average success rates and x2 longer the average distance until intervention than the baselines (ViNG and ReViND), proving that our method can significantly reduce the human intervention for practical applications. This project, called "*SCALE: Self-Correcting Visual Navigation for Mobile Robots via From 62f60a18c9c10cb55dd972a1311f7b376abb7a54 Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Thu, 29 Aug 2024 13:11:29 +0800 Subject: [PATCH 57/64] Update sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md Co-authored-by: Tomoya Fujita Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index 55e4a53..a77b45a 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -2,7 +2,7 @@ ## Background -Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. +Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route has been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. Through real-world testing, we observe that due to the limited state distribution in the offline dataset, when facing the **out-of-distribution (OOD)** observations induced by illumination and scenario changes and the accumulative errors, the localization loss issue often arises that the robot fails to localize itself on a given map. In this case, the robot may get stuck or make infeasible decision, which significantly decreases the navigation success rates and even results in collision. And this often requires tedious human monitoring and correction. To address this issue, our work poses the question and takes one step forward:’ can we leverage the prior knowledge from offline data to guide the robot to **self-correct** the trajectory autonomously without human intervention or specific data collection?’ To this end, our key idea is learning from offline dataset to predict whether future trajectories lie in the prior distribution, and plan the future trajectories that can drive the robot to the familiar places autonomously. Real-world experiments show our method achieves +26% average success rates and x2 longer the average distance until intervention than the baselines (ViNG and ReViND), proving that our method can significantly reduce the human intervention for practical applications. This project, called "*SCALE: Self-Correcting Visual Navigation for Mobile Robots via Anti-Novelty Estimation*", has been accepted by 2024 ICRA, see paper: https://arxiv.org/abs/2404.10675. From ccaab7307748489bffc4c613b04cfc6c1e50b5ae Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Thu, 29 Aug 2024 13:11:37 +0800 Subject: [PATCH 58/64] Update sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md Co-authored-by: Tomoya Fujita Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map.md | 1 + 1 file changed, 1 insertion(+) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index a77b45a..c77a0d5 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -55,6 +55,7 @@ The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned ![topological_navigation](./images/topological_navigation.png "topological_navigation" )--> ### Data Collection Module + We manually teleoperate the mobile robot to collect the trajectory datasets in the deployed scenarios. Commonly, a totally 30-minute dataset in each scenario is enough for training the navigation policy. In each trajectory of the dataset, the data consists of an onboard RGB image $o_t$ and the estimated pose $p_t = (x, y, θ)$ from odometry at each timestep. Note that the estimated pose in offline dataset is only used to calculate the robot action for offline training, while we only use RGB image as the observation during inference. ## References From 317d55da833679a14a87aeff0a0eec8dfab3f29c Mon Sep 17 00:00:00 2001 From: chch9907 <799783944@qq.com> Date: Thu, 29 Aug 2024 18:14:05 +0800 Subject: [PATCH 59/64] modify the proposal Signed-off-by: chch9907 <799783944@qq.com> --- ...ne Visual Navigation without Metric Map.md | 52 +++++++++---------- 1 file changed, 25 insertions(+), 27 deletions(-) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index c77a0d5..1cceeeb 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -10,51 +10,49 @@ Anti-Novelty Estimation*", has been accepted by 2024 ICRA, see paper: https://ar ## Goals For developers and end users, the goals of this project are: -- Building a navigation system with only visual input and without metric maps. The policy is trained by directly learning from real-world offline datasets that does not suffer from any sim-to-real gap. Furthermore, the system is robust to the OOD observations induced by illumination and scenario changes and accumulative error issues during navigation. -- Building a low-cost system without metric map by using purely vision input for supporting neural topological SLAM, which particularly can be easily integrated into an instruction-followed navigation tasks by grounding the languages into the visual semantics similar to LM-Nav [5]. -- Addressing the localization loss issue by the proposed self-correcting method so as to enable long-term navigation with little human intervention during the process. -- Providing an offline dataset collection method to enable the users to finetune on the new environments. +- Building a navigation system with only visual input and without metric maps. The policy is trained by directly learning from real-world offline datasets that does not suffer from any sim-to-real gap. +- Enabling the system to be low-cost by using purely vision input for supporting neural topological SLAM, and can be easily integrated into an instruction-followed navigation tasks by grounding the languages on the visual semantics similar to LM-Nav [5]. +- Enabling the system to be robust to the OOD observations induced by illumination and scenario changes and accumulative error issues using the proposed self-correcting method so as to achieve long-horizon navigation with little human intervention during the process. +- Providing an offline dataset collection method to enable the users to finetune on the new environments. Meanwhile, curating a large real-world trajectory dataset collected from diverse end devices in different scenarios, with different illuminations and seasons, and on different robots, which is absent in the current open-source datasets and is important to facilitate the models to be more generalized and robust. -## Proposal - -### Project scope +## Proposals -- This project considers the image-goal visual navigation task in both indoor and outdoor scenarios, which **inputs a RGB image and a specified goal image** and **outputs the actions (linear and angular velocities)** of the mobile robot at timestep t without LIDAR, GPS or IMU information. Its **downstream tasks** can include park delivery, mobile manipulation, or offline training a generic agent. +### Project Scope -- The method first collects some trajectories of the scene for pre-building a topological map where images as nodes and the number of traversing steps as edges for topological planning and localization. Then, the navigation system conducts image-goal conditioned travels in the scene while is robust against the localization lost issue. +- This project considers the image-goal visual navigation task in both indoor and outdoor scenarios, which **inputs a RGB image and a specified goal image** and **outputs the actions (linear and angular velocities)** of the mobile robots at timestep t without LIDAR, GPS or IMU information. Its **downstream tasks** can include park delivery, mobile manipulation, or offline training a generic agent. +- This project provides **both training and deployment pipelines** for using our method in the users' scenarios, while trained models can be used either on cloud or on edge since they are not too large for real-time inference. +- The method requires first collecting some trajectories of the scene for pre-building a topological map where images as nodes and the number of traversing steps as edges for topological planning and localization. Then, the navigation models are trained on the collected trajectoris, after which the complete system can conduct image-goal conditioned travels in the scene while is robust to the localization loss issue. -### Targeting users +### Targeting Users - Users that need a training pipeline of offline imitation learning or reinforcement learning that does not suffer from sim-to-real gap and can scale to handling larger datasets to achieve a generic AI agent similar to ViNT [3]. -- Users that need a visual navigation system in the deploying environments to implement image-goal navigation tasks such as last-mile delivering given an image as a goal. +- Users that need a visual navigation system in the deploying environments to implement image-goal navigation tasks such as park delivering given an image as a goal. - Users that need a non-metric map representation, i.e., an image-node topological graph for long-horizon planning and localization with low storing and updating cost. -- Developers that want to build a customized visual navigation systems for mobile or mobile manipulation tasks on the robots. +- Developers that want to build a customized visual navigation system for mobile or mobile manipulation tasks according to their preference. ## Design Details + ### Architecture Design ![framework](./images/framework.png "overall framework") -**Fig. 1. Overall framework.** +**Fig. 1. Overall pipelines for offline training and online inference.** (a) SCALE first pretrains a self-consistent representation space $z$ by the VAE-style loss, then fine-tunes it by the gradients from $Q(s, a, g)$ in the IQL. Next, we train the policy network $π$, temporal affordance model $ψ$ and the novelty predictor $f_ω$ over the trained representation space. -(b) When the robot gets lost, SCALE randomly samples some transition $u$ from the prior $p(u)$ and feds it to the temporal affordance model to generate some multi-step latent trajectories recursively. Then it evaluates the candidates in terms of the reachability, anti-novelty and aggressiveness. Finally, it selects the optimal trajectory and executes the first step, then repeats until being successfully localized again. +(b) During inference, when the robot gets lost, SCALE randomly samples some transition $u$ from the prior $p(u)$ and feds it to the temporal affordance model to generate some multi-step latent trajectories recursively. Then it evaluates the candidates in terms of the reachability, anti-novelty and aggressiveness. Finally, it selects the optimal trajectory and executes the first step, then repeats until being successfully localized again. -The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned visual navigation policy and value function, 2) a neural topological SLAM module, and 3) a self-correcting module for autonomously recovering the localization. +The complete navigation system consists of 1) an image-goal conditioned visual navigation policy and value function, 2) a self-correcting module for autonomously recovering the localization and 3) a neural topological SLAM module, where the pipelines of the first two terms are illustrated in Fig. 1. -- **Image-goal conditioned visual navigation**: we use implicit Q-learning (IQL) [6], an offline reinforcement learning method to learn the value function and policy from offline dataset. In practice, we find two techniques useful for successfully learning the value functions from purely visual input: 1) *negative sampling* is necessary for value learning with paired image inputs, where we assign image pairs that are separated less than or equal to a threshold of timesteps $d_{max}$ as the positive samples $B_+$ and those separated beyond the threshold are assigned as the negative samples $B_−$. 2) *relative goal embedding*, the difference between goal and current image embeddings, *i.e.*, $∆z_{g,t} = z_g – z_t$, is more effective than directly using goal embedding $z_g$ as the input for the goalconditioned networks to perceive the goal orientation. -- **Self-correction for localization recovery**: we self-supervisedly learn an affordance model from offline dataset for generating potential future trajectories in the latent space. On the one hand, to predict multi-step trajectories, we use conditional generation model to learn the latent space with forward-inverse cycle consistency (FICC). On the other hand, we take advantage of the surrounding pixels in history frames using recurrent neural network (RNN) to predict some aggressive trajectories that are largely beyond the current field-of-view. Furthermore, we learn a novelty estimator via random network distillation (RND) [7] technique to evaluate the predicted future states. Intuitively, the reachability and aggressiveness facilitate the reasonability and diversity, while the anti-novelty strategy can induce the robot to the familiar places. -- **Neural topological SLAM**: we construct a topological map in the environment where each node is a previously observed image and each edge refers to the traversability between two nodes that is estimated by the learned value function. The localization process is conducted by searching the highest value prediction between current image and all the images on the map. An example of the integration of navigation, neural topological SLAM, and localization recovery module is illustrated in Fig.2. -
      -architecture +- **Image-goal conditioned visual navigation**: we use implicit Q-learning (IQL) [6], an offline reinforcement learning method to learn the value function and policy from offline dataset. In practice, we find two techniques useful for successfully learning the value functions from purely visual input: 1) *Negative sampling* is necessary for value learning with paired image inputs, where we assign image pairs that are separated less than or equal to a threshold of timesteps $d_{max}$ as the positive samples $B_+$ and those separated beyond the threshold are assigned as the negative samples $B_−$. 2) *Relative goal embedding*, the difference between goal and current image embeddings, *i.e.*, $∆z_{g,t} = z_g – z_t$, is more effective than directly using goal embedding $z_g$ as the input for the goalconditioned networks to perceive the goal orientation. +- **Self-correction for localization recovery**: we self-supervisedly learn an affordance model from offline dataset for generating potential future trajectories in the latent space. On the one hand, to predict multi-step trajectories, we use conditional generation model to learn the latent space with forward-inverse cycle consistency (FICC). On the other hand, we take advantage of the surrounding pixels in history frames using recurrent neural network (RNN) to predict some aggressive trajectories that are largely beyond the current field-of-view. Furthermore, we learn a novelty estimator via random network distillation (RND) [7] technique to evaluate the predicted future states. Intuitively, the reachability and aggressiveness facilitate the reasonability and diversity, while the anti-novelty strategy can induce the robot to the familiar places. +- **Neural topological SLAM**: we construct a topological map in the environment where each node is a previously observed image and each edge refers to the traversability between two nodes that is estimated by the learned value function. The localization process is conducted by searching the highest value prediction between current image and all the images on the map. + +An example of the integration of visual navigation, neural topological SLAM, and localization recovery modules is illustrated in Fig.2. +
      + architecture
      +**Fig. 2. Visual navigation with neural topological SLAM and localization recovery**. SCALE combines the topological visual navigation with a novel localization recovery module. We first build a topological map (gray cycles and lines) based on the offline dataset. Next, starting at the yellow cycle, we use the localization module to do active initialization. Then, given a goal image, we search a route (orange cycles) on the topological graph and execute to the goal (red cycle) step by step. - **Fig. 2. Topological navigation with localization recovery**. SCALE combines the topological visual navigation with a novel localization recovery module. - - -### Data Collection Module +### Data Collection We manually teleoperate the mobile robot to collect the trajectory datasets in the deployed scenarios. Commonly, a totally 30-minute dataset in each scenario is enough for training the navigation policy. In each trajectory of the dataset, the data consists of an onboard RGB image $o_t$ and the estimated pose $p_t = (x, y, θ)$ from odometry at each timestep. Note that the estimated pose in offline dataset is only used to calculate the robot action for offline training, while we only use RGB image as the observation during inference. From 729b16b00398679ae6d7148f4e67f8bc47c4b8c4 Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Sat, 24 Aug 2024 12:41:57 +0800 Subject: [PATCH 60/64] Create Offline Visual Navigation without Metric Map.md add scale proposal Signed-off-by: chch9907 <799783944@qq.com> --- ...ne Visual Navigation without Metric Map.md | 146 +----------------- 1 file changed, 1 insertion(+), 145 deletions(-) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index 1cceeeb..3201cd8 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -63,148 +63,4 @@ We manually teleoperate the mobile robot to collect the trajectory datasets in t [4] D.S. Chaplot et al., “Neural Topological SLAM for Visual Navigation”, 2020 [5] D. Shah et al., “LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action”, 2022 [6] Kostrikov et al., “Offline Reinforcement Learning with Implicit Q-Learning”, 2021. -[7] Burda et al., “Exploration by Random Network Distillation”, 2018. -# Offline Visual Navigation without Metric Map - -## Background -Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. - -Through real-world testing, we observe that due to the limited state distribution in the offline dataset, when facing the **out-of-distribution (OOD)** observations induced by illumination and scenario changes and the accumulative errors, the localization loss issue often arises that the robot fails to localize itself on a given map. In this case, the robot may get stuck or make infeasible decision, which significantly decreases the navigation success rates and even results in collision. And this often requires tedious human monitoring and correction. To address this issue, our work poses the question and takes one step forward:’ can we leverage the prior knowledge from offline data to guide the robot to **self-correct** the trajectory autonomously without human intervention or specific data collection?’ To this end, our key idea is learning from offline dataset to predict whether future trajectories lie in the prior distribution, and plan the future trajectories that can drive the robot to the familiar places autonomously. Real-world experiments show our method achieves +26% average success rates and x2 longer the average distance until intervention than the baselines (ViNG and ReViND), proving that our method can significantly reduce the human intervention for practical applications. This project, called "*SCALE: Self-Correcting Visual Navigation for Mobile Robots via -Anti-Novelty Estimation*", has been accepted by 2024 ICRA, see paper: https://arxiv.org/abs/2404.10675. - -## Goals - -For developers and end users, the goals of this project are: -- Building a navigation system with only visual input and without metric maps. The policy is trained by directly learning from real-world offline datasets that does not suffer from any sim-to-real gap. Furthermore, the system is robust to the OOD observations induced by illumination and scenario changes and accumulative error issues during navigation. -- Building a low-cost system without metric map by using purely vision input for supporting neural topological SLAM, which particularly can be easily integrated into an instruction-followed navigation tasks by grounding the languages into the visual semantics similar to LM-Nav [5]. -- Addressing the localization loss issue by the proposed self-correcting method so as to enable long-term navigation with little human intervention during the process. -- Providing an offline dataset collection method to enable the users to finetune on the new environments. - -## Proposal - -### Project scope -- This project considers the image-goal visual navigation task in both indoor and outdoor scenarios, which **inputs a RGB image and a specified goal image** and **outputs the actions (linear and angular velocities)** of the mobile robot at timestep t without LIDAR, GPS or IMU information. Its **downstream tasks** can include park delivery, mobile manipulation, or offline training a generic agent. - -- The method first collects some trajectories of the scene for pre-building a topological map where images as nodes and the number of traversing steps as edges for topological planning and localization. Then, the navigation system conducts image-goal conditioned travels in the scene while is robust against the localization lost issue. - -### Targeting users - -- Users that need a training pipeline of offline imitation learning or reinforcement learning that does not suffer from sim-to-real gap and can scale to handling larger datasets to achieve a generic AI agent similar to ViNT [3]. -- Users that need a visual navigation system in the deploying environments to implement image-goal navigation tasks such as last-mile delivering given an image as a goal. -- Users that need a non-metric map representation, i.e., an image-node topological graph for long-horizon planning and localization with low storing and updating cost. -- Developers that want to build a customized visual navigation systems for mobile or mobile manipulation tasks on the robots. - -## Design Details -### Architecture Design - - - -![framework](./images/framework.png "overall framework") -**Fig. 1. Overall framework.** (a) SCALE first pretrains a self-consistent representation space $z$ by the VAE-style loss, then fine-tunes it by the gradients from $Q(s, a, g)$ in the IQL. Next, we train the policy network $π$, temporal affordance model $ψ$ and the novelty predictor $f_ω$ over the trained representation space. (b) When the robot gets lost, SCALE randomly samples some transition $u$ from the prior $p(u)$ and feds it to the temporal affordance model to generate some multi-step latent trajectories recursively. Then it evaluates the candidates in terms of the reachability, anti-novelty and aggressiveness. Finally, it selects the optimal trajectory and executes the first step, then repeats until being successfully localized again. - - -The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned visual navigation policy and value function, 2) a neural topological SLAM module, and 3) a self-correcting module for autonomously recovering the localization. - -- **Image-goal conditioned visual navigation**: we use implicit Q-learning (IQL) [6], an offline reinforcement learning method to learn the value function and policy from offline dataset. In practice, we find two techniques useful for successfully learning the value functions from purely visual input: 1) *negative sampling* is necessary for value learning with paired image inputs, where we assign image pairs that are separated less than or equal to a threshold of timesteps $d_{max}$ as the positive samples $B_+$ and those separated beyond the threshold are assigned as the negative samples $B_−$. 2) *relative goal embedding*, the difference between goal and current image embeddings, *i.e.*, $∆z_{g,t} = z_g – z_t$, is more effective than directly using goal embedding $z_g$ as the input for the goalconditioned networks to perceive the goal orientation. -- **Self-correction for localization recovery**: we self-supervisedly learn an affordance model from offline dataset for generating potential future trajectories in the latent space. On the one hand, to predict multi-step trajectories, we use conditional generation model to learn the latent space with forward-inverse cycle consistency (FICC). On the other hand, we take advantage of the surrounding pixels in history frames using recurrent neural network (RNN) to predict some aggressive trajectories that are largely beyond the current field-of-view. Furthermore, we learn a novelty estimator via random network distillation (RND) [7] technique to evaluate the predicted future states. Intuitively, the reachability and aggressiveness facilitate the reasonability and diversity, while the anti-novelty strategy can induce the robot to the familiar places. -- **Neural topological SLAM**: we construct a topological map in the environment where each node is a previously observed image and each edge refers to the traversability between two nodes that is estimated by the learned value function. The localization process is conducted by searching the highest value prediction between current image and all the images on the map. An example of the integration of navigation, neural topological SLAM, and localization recovery module is illustrated in Fig.2. -
      -architecture -
      - - **Fig. 2. Topological navigation with localization recovery**. SCALE combines the topological visual navigation with a novel localization recovery module. - - - - - - - - -### Data Collection Module -We manually teleoperate the mobile robot to collect the trajectory datasets in the deployed scenarios. Commonly, a totally 30-minute dataset in each scenario is enough for training the navigation policy. In each trajectory of the dataset, the data consists of an onboard RGB image $o_t$ and the estimated pose $p_t = (x, y, θ)$ from odometry at each timestep. Note that the estimated pose in offline dataset is only used to calculate the robot action for offline training, while we only use RGB image as the observation during inference. - - -## References -[1] D. Shah et al., “ViNG: Learning Open-World Navigation with Visual Goals”, 2021. -[2] D. Shah et al., “Offline Reinforcement Learning for Visual Navigation”, 2022. -[3] D. Shah et al., “ViNT: A Foundation Model for Visual Navigation”, 2023. -[4] D.S. Chaplot et al., “Neural Topological SLAM for Visual Navigation”, 2020 -[5] D. Shah et al., “LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action”, 2022 -[6] Kostrikov et al., “Offline Reinforcement Learning with Implicit Q-Learning”, 2021. -[7] Burda et al., “Exploration by Random Network Distillation”, 2018. -# Offline Visual Navigation without Metric Map - -## Background -Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. - -Through real-world testing, we observe that due to the limited state distribution in the offline dataset, when facing the **out-of-distribution (OOD)** observations induced by illumination and scenario changes and the accumulative errors, the localization loss issue often arises that the robot fails to localize itself on a given map. In this case, the robot may get stuck or make infeasible decision, which significantly decreases the navigation success rates and even results in collision. And this often requires tedious human monitoring and correction. To address this issue, our work poses the question and takes one step forward:’ can we leverage the prior knowledge from offline data to guide the robot to **self-correct** the trajectory autonomously without human intervention or specific data collection?’ To this end, our key idea is learning from offline dataset to predict whether future trajectories lie in the prior distribution, and plan the future trajectories that can drive the robot to the familiar places autonomously. Real-world experiments show our method achieves +26% average success rates and x2 longer the average distance until intervention than the baselines (ViNG and ReViND), proving that our method can significantly reduce the human intervention for practical applications. This project, called "*SCALE: Self-Correcting Visual Navigation for Mobile Robots via -Anti-Novelty Estimation*", has been accepted by 2024 ICRA, see paper: https://arxiv.org/abs/2404.10675. - -## Goals - -For developers and end users, the goals of this project are: -- Building a navigation system with only visual input and without metric maps. The policy is trained by directly learning from real-world offline datasets that does not suffer from any sim-to-real gap. Furthermore, the system is robust to the OOD observations induced by illumination and scenario changes and accumulative error issues during navigation. -- Building a low-cost system without metric map by using purely vision input for supporting neural topological SLAM, which particularly can be easily integrated into an instruction-followed navigation tasks by grounding the languages into the visual semantics similar to LM-Nav [5]. -- Addressing the localization loss issue by the proposed self-correcting method so as to enable long-term navigation with little human intervention during the process. -- Providing an offline dataset collection method to enable the users to finetune on the new environments. - -## Proposal - -### Project scope -- This project considers the image-goal visual navigation task in both indoor and outdoor scenarios, which **inputs a RGB image and a specified goal image** and **outputs the actions (linear and angular velocities)** of the mobile robot at timestep t without LIDAR, GPS or IMU information. Its **downstream tasks** can include park delivery, mobile manipulation, or offline training a generic agent. - -- The method first collects some trajectories of the scene for pre-building a topological map where images as nodes and the number of traversing steps as edges for topological planning and localization. Then, the navigation system conducts image-goal conditioned travels in the scene while is robust against the localization lost issue. - -### Targeting users - -- Users that need a training pipeline of offline imitation learning or reinforcement learning that does not suffer from sim-to-real gap and can scale to handling larger datasets to achieve a generic AI agent similar to ViNT [3]. -- Users that need a visual navigation system in the deploying environments to implement image-goal navigation tasks such as last-mile delivering given an image as a goal. -- Users that need a non-metric map representation, i.e., an image-node topological graph for long-horizon planning and localization with low storing and updating cost. -- Developers that want to build a customized visual navigation systems for mobile or mobile manipulation tasks on the robots. - -## Design Details -### Architecture Design - - - -![framework](./images/framework.png "overall framework") -**Fig. 1. Overall framework.** (a) SCALE first pretrains a self-consistent representation space $z$ by the VAE-style loss, then fine-tunes it by the gradients from $Q(s, a, g)$ in the IQL. Next, we train the policy network $π$, temporal affordance model $ψ$ and the novelty predictor $f_ω$ over the trained representation space. (b) When the robot gets lost, SCALE randomly samples some transition $u$ from the prior $p(u)$ and feds it to the temporal affordance model to generate some multi-step latent trajectories recursively. Then it evaluates the candidates in terms of the reachability, anti-novelty and aggressiveness. Finally, it selects the optimal trajectory and executes the first step, then repeats until being successfully localized again. - - -The framework is shown in Fig. 1, which consists of 1) an image-goal conditioned visual navigation policy and value function, 2) a neural topological SLAM module, and 3) a self-correcting module for autonomously recovering the localization. - -- **Image-goal conditioned visual navigation**: we use implicit Q-learning (IQL) [6], an offline reinforcement learning method to learn the value function and policy from offline dataset. In practice, we find two techniques useful for successfully learning the value functions from purely visual input: 1) *negative sampling* is necessary for value learning with paired image inputs, where we assign image pairs that are separated less than or equal to a threshold of timesteps $d_{max}$ as the positive samples $B_+$ and those separated beyond the threshold are assigned as the negative samples $B_−$. 2) *relative goal embedding*, the difference between goal and current image embeddings, *i.e.*, $∆z_{g,t} = z_g – z_t$, is more effective than directly using goal embedding $z_g$ as the input for the goalconditioned networks to perceive the goal orientation. -- **Self-correction for localization recovery**: we self-supervisedly learn an affordance model from offline dataset for generating potential future trajectories in the latent space. On the one hand, to predict multi-step trajectories, we use conditional generation model to learn the latent space with forward-inverse cycle consistency (FICC). On the other hand, we take advantage of the surrounding pixels in history frames using recurrent neural network (RNN) to predict some aggressive trajectories that are largely beyond the current field-of-view. Furthermore, we learn a novelty estimator via random network distillation (RND) [7] technique to evaluate the predicted future states. Intuitively, the reachability and aggressiveness facilitate the reasonability and diversity, while the anti-novelty strategy can induce the robot to the familiar places. -- **Neural topological SLAM**: we construct a topological map in the environment where each node is a previously observed image and each edge refers to the traversability between two nodes that is estimated by the learned value function. The localization process is conducted by searching the highest value prediction between current image and all the images on the map. An example of the integration of navigation, neural topological SLAM, and localization recovery module is illustrated in Fig.2. -
      -architecture -
      - - **Fig. 2. Topological navigation with localization recovery**. SCALE combines the topological visual navigation with a novel localization recovery module. - - - - - - - - -### Data Collection Module -We manually teleoperate the mobile robot to collect the trajectory datasets in the deployed scenarios. Commonly, a totally 30-minute dataset in each scenario is enough for training the navigation policy. In each trajectory of the dataset, the data consists of an onboard RGB image $o_t$ and the estimated pose $p_t = (x, y, θ)$ from odometry at each timestep. Note that the estimated pose in offline dataset is only used to calculate the robot action for offline training, while we only use RGB image as the observation during inference. - - -## References -[1] D. Shah et al., “ViNG: Learning Open-World Navigation with Visual Goals”, 2021. -[2] D. Shah et al., “Offline Reinforcement Learning for Visual Navigation”, 2022. -[3] D. Shah et al., “ViNT: A Foundation Model for Visual Navigation”, 2023. -[4] D.S. Chaplot et al., “Neural Topological SLAM for Visual Navigation”, 2020 -[5] D. Shah et al., “LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action”, 2022 -[6] Kostrikov et al., “Offline Reinforcement Learning with Implicit Q-Learning”, 2021. -[7] Burda et al., “Exploration by Random Network Distillation”, 2018. +[7] Burda et al., “Exploration by Random Network Distillation”, 2018. \ No newline at end of file From 37df8044f9cd0dd7f9569a3c76051c52db66e5f2 Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Sat, 24 Aug 2024 13:08:10 +0800 Subject: [PATCH 61/64] add Offline Visual Navigation without Metric Map Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map/images/test.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt new file mode 100644 index 0000000..e8689b2 --- /dev/null +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt @@ -0,0 +1 @@ +hh From 05a19d94be04950d3ca4945f3052ce6172377a4a Mon Sep 17 00:00:00 2001 From: chang chen <57316606+chch9907@users.noreply.github.com> Date: Sat, 24 Aug 2024 13:09:35 +0800 Subject: [PATCH 62/64] Delete sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map/images/test.txt | 1 - 1 file changed, 1 deletion(-) delete mode 100644 sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt deleted file mode 100644 index e8689b2..0000000 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/images/test.txt +++ /dev/null @@ -1 +0,0 @@ -hh From 3ec570c8c56ac830337878f4fcbdec74c997f549 Mon Sep 17 00:00:00 2001 From: chch9907 <799783944@qq.com> Date: Thu, 29 Aug 2024 21:59:43 +0800 Subject: [PATCH 63/64] modify the proposal Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map.md | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index 3201cd8..06bf293 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -10,9 +10,9 @@ Anti-Novelty Estimation*", has been accepted by 2024 ICRA, see paper: https://ar ## Goals For developers and end users, the goals of this project are: -- Building a navigation system with only visual input and without metric maps. The policy is trained by directly learning from real-world offline datasets that does not suffer from any sim-to-real gap. -- Enabling the system to be low-cost by using purely vision input for supporting neural topological SLAM, and can be easily integrated into an instruction-followed navigation tasks by grounding the languages on the visual semantics similar to LM-Nav [5]. -- Enabling the system to be robust to the OOD observations induced by illumination and scenario changes and accumulative error issues using the proposed self-correcting method so as to achieve long-horizon navigation with little human intervention during the process. +- Building a image-goal conditioned visual navigation system with only visual input and without metric maps. The models are trained by directly learning from real-world offline datasets that does not suffer from any sim-to-real gap. +- The system can be easily integrated into an instruction-followed navigation tasks by grounding the languages on the visual semantics similar to LM-Nav [5]. +- The system can be robust to the OOD observations induced by illumination and scenario changes and accumulative error issues using the proposed self-correcting method so as to achieve long-horizon navigation with little human intervention during the process. - Providing an offline dataset collection method to enable the users to finetune on the new environments. Meanwhile, curating a large real-world trajectory dataset collected from diverse end devices in different scenarios, with different illuminations and seasons, and on different robots, which is absent in the current open-source datasets and is important to facilitate the models to be more generalized and robust. ## Proposals @@ -45,11 +45,12 @@ The complete navigation system consists of 1) an image-goal conditioned visual n - **Self-correction for localization recovery**: we self-supervisedly learn an affordance model from offline dataset for generating potential future trajectories in the latent space. On the one hand, to predict multi-step trajectories, we use conditional generation model to learn the latent space with forward-inverse cycle consistency (FICC). On the other hand, we take advantage of the surrounding pixels in history frames using recurrent neural network (RNN) to predict some aggressive trajectories that are largely beyond the current field-of-view. Furthermore, we learn a novelty estimator via random network distillation (RND) [7] technique to evaluate the predicted future states. Intuitively, the reachability and aggressiveness facilitate the reasonability and diversity, while the anti-novelty strategy can induce the robot to the familiar places. - **Neural topological SLAM**: we construct a topological map in the environment where each node is a previously observed image and each edge refers to the traversability between two nodes that is estimated by the learned value function. The localization process is conducted by searching the highest value prediction between current image and all the images on the map. -An example of the integration of visual navigation, neural topological SLAM, and localization recovery modules is illustrated in Fig.2. +An example of the integration of visual navigation, neural topological SLAM, and localization recovery modules is illustrated in Fig.2. Our method combines the topological visual navigation with a novel localization recovery module. We first build a topological map (gray cycles and lines) based on the offline dataset. Next, starting at the yellow cycle, we use the localization module to do active initialization. Then, given a goal image, we search a route (orange cycles) on the topological graph and execute to the goal (red cycle) step by step.
      architecture
      -**Fig. 2. Visual navigation with neural topological SLAM and localization recovery**. SCALE combines the topological visual navigation with a novel localization recovery module. We first build a topological map (gray cycles and lines) based on the offline dataset. Next, starting at the yellow cycle, we use the localization module to do active initialization. Then, given a goal image, we search a route (orange cycles) on the topological graph and execute to the goal (red cycle) step by step. + +**Fig. 2. Visual navigation with neural topological SLAM and localization recovery**. ### Data Collection From ca06fd1119774e44433feb7c8b92879575f9419b Mon Sep 17 00:00:00 2001 From: chch9907 <799783944@qq.com> Date: Wed, 11 Sep 2024 15:50:57 +0800 Subject: [PATCH 64/64] modify proposal Signed-off-by: chch9907 <799783944@qq.com> --- .../Offline Visual Navigation without Metric Map.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md index 06bf293..bbf16cb 100644 --- a/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md +++ b/sig-robotics/proposal/Offline Visual Navigation without Metric Map/Offline Visual Navigation without Metric Map.md @@ -5,12 +5,13 @@ Existing learning-based navigation studies mainly develop the algorithms in simulation environment that often suffer from **sim-to-real gap** issue when transferring to the real world. Recently, there is some work dedicated to lift the simulation assumptions by learning directly from offline real-world dataset that are not necessarily collected from experts, such as ViNG [1], ReViND [2], and ViNT [3], which use either imitation learning (IL) or offline reinforcement learning (ORL). This route has been proven to achieve broader generalization for executing real-world tasks and thereby step towards generic AI agents. Moreover, the efforts in navigation tasks integrate offline learned policy with an image-node neural topological SLAM [4] for handling long-horizon reasoning that pure RL policy cannot tackle, which has a significance value for replacing the conventional metric maps that are hard to store and update. Therefore, this work aims to follow this technical route and explore how to achieve a **robust** AI agent in the navigation tasks **without sim-to-real gap** and **without metric maps**. Through real-world testing, we observe that due to the limited state distribution in the offline dataset, when facing the **out-of-distribution (OOD)** observations induced by illumination and scenario changes and the accumulative errors, the localization loss issue often arises that the robot fails to localize itself on a given map. In this case, the robot may get stuck or make infeasible decision, which significantly decreases the navigation success rates and even results in collision. And this often requires tedious human monitoring and correction. To address this issue, our work poses the question and takes one step forward:’ can we leverage the prior knowledge from offline data to guide the robot to **self-correct** the trajectory autonomously without human intervention or specific data collection?’ To this end, our key idea is learning from offline dataset to predict whether future trajectories lie in the prior distribution, and plan the future trajectories that can drive the robot to the familiar places autonomously. Real-world experiments show our method achieves +26% average success rates and x2 longer the average distance until intervention than the baselines (ViNG and ReViND), proving that our method can significantly reduce the human intervention for practical applications. This project, called "*SCALE: Self-Correcting Visual Navigation for Mobile Robots via -Anti-Novelty Estimation*", has been accepted by 2024 ICRA, see paper: https://arxiv.org/abs/2404.10675. +Anti-Novelty Estimation*", has been accepted by 2024 ICRA, see paper: https://ieeexplore.ieee.org/document/10610847. + ## Goals For developers and end users, the goals of this project are: -- Building a image-goal conditioned visual navigation system with only visual input and without metric maps. The models are trained by directly learning from real-world offline datasets that does not suffer from any sim-to-real gap. +- Building an image-goal conditioned visual navigation system with only visual input and without metric maps. The models are trained by directly learning from real-world offline datasets that does not suffer from any sim-to-real gap. - The system can be easily integrated into an instruction-followed navigation tasks by grounding the languages on the visual semantics similar to LM-Nav [5]. - The system can be robust to the OOD observations induced by illumination and scenario changes and accumulative error issues using the proposed self-correcting method so as to achieve long-horizon navigation with little human intervention during the process. - Providing an offline dataset collection method to enable the users to finetune on the new environments. Meanwhile, curating a large real-world trajectory dataset collected from diverse end devices in different scenarios, with different illuminations and seasons, and on different robots, which is absent in the current open-source datasets and is important to facilitate the models to be more generalized and robust.

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