This repository offers the official implementation of Chain-of-Action on RLBench. The framework aims to integrate representative visuomotor policies, including the ACT and Diffusion Policy (To-do soon), with standardized training and evaluation protocols to facilitate fair comparison and reproducibility for the community.

conda create -n coa python=3.9 -y
conda activate coa
bash scripts/init.sh
source ~/.bashrc

install dependencies and RLBench enviroment, see init.sh for details

The script will automatically download the required pretrained snapshot and the necessary evaluation dataset for the specified task.

bash scripts/eval.sh task=push_button

Execute the command to download all data.

python scripts/download_dataset.py

python scripts/download_dataset.py --task reach_target --train-episodes 100 --eval-episodes 25

• --task: Specify the task name to download (e.g., reach_target, stack_wine). Only one task will be downloaded.
• --train-episodes: Number of training episodes per task (default: 100, total 100).
• --eval-episodes: Number of evaluation episodes per task (default: 25, total 50).
• To download the recommended 10-task subset, add the --subset flag. To download all tasks, do not specify --task or --subset.

python scripts/eval.py task=task_name snapshot=path_to_snapshot

python scripts/train.py task=task_name 

For detailed parameter settings, please refer to launch.yaml.

Key parameters include:

• num_train_steps: total training steps (default: 20000)
• batch_size: training batch size (default: 128)
• task: task name (must be specified)
• demos: number of demonstrations per task (default: 100)
• eval_every_steps: evaluation interval in steps (default: 10000)
• vis_every_steps: visualization interval in steps (default: 2000)
• save_every_steps: model checkpoint interval in steps (default: 10000)
• num_eval_episodes: number of episodes per evaluation (default: 25)

You can customize these parameters by editing src/cfgs/launch.yaml directly, or override them via command line arguments (e.g., python scripts/train.py task=push_button batch_size=64).

• scripts/：Training, evaluation, data/snapshot downloading scripts

• src/: Main source code directory, including the following subfolders and files:

  • cfgs/: Configuration files
  • dataset/: Dataset loading and preprocessing
  • envs/: Simulation environment
  • methods/: Algorithm implementations
    • coa/: Chain-of-Action
    • act/: ACT (To-do)
    • dp/: Diffusion policy (To-do)
    • base.py, utils.py: Common base classes and utilities
  • utils.py, logger.py, video.py, : General utilities and main control scripts
  • workspace.py: training workflow

• exp_local/: Local experiment results.

  • checkpoints/: Model weights
  • eval_videos/: Evaluation videos
  • train.log: Training log
  • .hydra/: Configuration snapshots and Hydra management files

• README.md：Project documentation

@inproceedings{zhang2025chainofaction,
  author={Zhang, Wenbo and Hu, Tianrun and Qiao, Yanyuan and Zhang, Hanbo and Qin, Yuchu and Li, Yang and Liu, Jiajun and Kong, Tao and Liu, Lingqiao and Ma, Xiao},
  title={Chain-of-Action: Trajectory Autoregressive Modeling for Robotic Manipulation},
  journal= {arxiv},
  year={2025},
}

This project is licensed under the Apache License, Version 2.0. See the LICENSE file for details.