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Hierarchical-ACT

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J. Hyeon Park, Wonhyuk Choi, Sunpyo Hong, Hoseong Seo, Joonmo Ahn, Changsu Ha, Heungwoo Han, Junghyun Kwon, and Sungchul Kang "Hierarchical Action Chunk Transformer: Learning Temporal Multimodality from Demonstrations with Fast Imitation Behavior", IEEE International Conference on Intelligent Robots and Systems (IROS), 2024

Experiment video

Stack cups


Close pocket


Open lid


List of maintainers

Installation

conda create -n robot_action_learner python=3.8
conda activate robot_action_learner
pip install -r requirements.txt

Train with sample data

python ./train.py \
    --exp_dir ./experiment \
    --data_root ./data_samples \
    --data_config ./configs/dataset/srrc_dual_frankas/stack_cups.gin \
    --model_config ./configs/model/hact_vq/srrc_dual_frankas/base.gin \
    --task_config ./configs/task/hact_vq/srrc_dual_frankas/base_local.gin \
    --gpu 0
  • model.pt will be created in exp_dir.

Create agent

python ./serve.py --exp_dir ./experiment
  • agent.pt will be created in exp_dir.

Deploy agent

agent = torch.jit.load("./experiment/agent.pt")
agent = agent.eval()

obs = env.reset()
while not done:
    action = agent.forward(obs, timestep=t)
    obs, reward, done, truncated, info = env.step(action)

Todo

  • We will open the simulation (Viper-X) and datasets soon.

Code information

This code is implemented on the top of ACT. Several codes are adopted or modified from ACT.

Acknowledgement

All members of our remarkable robotics team: Joonmo Ahn, Rakjoon Chung, Changsu Ha, Heungwoo Han, Sunpyo Hong, Jaesik Jang, Rijeong Kang, Hosang Lee, Dongwoo Park, Hoseong Seo, Jaemin Yoon (in alphabetic order)