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Housekeep

This repository contains the implementation of our ECCV 2022 paper Housekeep: Tidying Virtual Households using Commonsense Reasoning

teaser

Dependencies Installation

  • Setup Python conda create -n habitat python=3.7 cmake=3.14.0
  • Install habitat-sim conda install habitat-sim==0.2.1 withbullet headless -c conda-forge -c aihabitat
  • Clone this repo and get all submodules for this repo: git submodule init, git submodule update

Habitat Lab

  • Go into Habitat Lab directory cd habitat-lab
  • Install requirements pip install -r requirements.txt
  • Install development version of Habitat Lab python setup.py develop --all

Additional Installs

  • Install A* path planner cd cos_eor/explore/astar_pycpp && make && cd -
  • Install additional requirements with pip install -r requirements.txt in the base directory

Data

Find the instructions for downloading object models from different sources and for setting up our data directory here.

The file cos_eor/scripts/dump/scale_rots_all.npy contains metadata information for the complete collection of object models. It includes the scales and rotations to use for placing these object models appropriately in the scenes.

Visuals

The videos we showed to the participants for collecting annotations.

Visualization of object models by source.

Running the baseline

  • Generate config files for running the experiments ./generate_configs.sh <exp_name> <explore_type> <rank_type> <explore_type> is the type of exploration module to use: phasic or oracle <rank_type> is the ranking function to use: LangModel or Oracle

    This will create configuration files for all scenes inside ./logs/<exp_name>/configs. Edit the configuration files to change other properties.

  • Run hierarchical policy on a scene (eg. ihlen_1_int) ./run_cli.sh <exp_name> <scene_name> <num_episodes> The experiment logs will be written inside logs/<exp_name> directory.

Aggregating results

Use the following command to aggregate metrics across scenes and save the aggregated values (average values with standard deviations) to a file: python cos_eor/scripts/aggregrate_metrics.py --metrics-files logs/<exp_name>/metrics-{<scene_names>}* --subsplit <subsplit> --out-file <save_path>.
<scene_names> is a comma separated list of scene names. Set this to ihlen_1_int,merom_1_int when evaluating on val scenes and beechwood_1_int,benevolence_1_int,ihlen_0_int,merom_0_int when evaluating on test scenes.
Use <subsplit> (set to either all, seen or unseen) to indicate if the metrics are to be aggregated across all episodes or across only the episodes containing seen or unseen objects.

Evaluating object-room (OR) and object-room-receptacle (ORR) matching scores

Use the cos_eor/scripts/orm/evaluate_scores.py script to evaluate your OR/ORR scores and to reproduce the mAP values reported in the paper.

Citing

If you find our work useful for your research, please consider citing:

@misc{kant2022housekeep,
            title={Housekeep: Tidying Virtual Households using Commonsense Reasoning},
            author={Yash Kant and Arun Ramachandran and Sriram Yenamandra and Igor Gilitschenski and Dhruv Batra and Andrew Szot and Harsh Agrawal},
            year={2022},
            eprint={2205.10712},
            archivePrefix={arXiv},
            primaryClass={cs.CV}
}