Autoregressive Action Sequence Learning for Robotic Manipulation

We present an imitation learning architecture based on autoregressive action sequence learning. We demonstrate strong results on Push-T, ALOHA, RLBench, and real robot experiments. For details, please check our paper.

github-demo.mp4

Getting Started

To install, clone this repository and recreate the python environment according to ENV.md, and download datasets and pretrained models according to Download.md.

• To evaluate or run demonstration with pretrained models, follow the instructions in Eval.md.

• To train ARP in Push-T, ALOHA, or RLBench, follow the instructions in Train.md.

More Experiments

1. To count MACs and parameters, please check profile.ipynb.

2. To run baselines and ablation studies, please check Experiments.md. We also provide a much cleaner implementation of RVT-2.

3. Please check real-robot/readme.ipynb, if you want to learn more about the real robot experiment.

4. Visualization on Likelihood Inference and Prediction with Human Guidance. Please check pusht/qualitative-visualize.ipynb.

5. If you look for supplementary video, please check the videos folder in https://rutgers.box.com/s/uzozemx67kje58ycy3lyzf1zgddz8tyq.

6. arp.py is a single-file implementation of our autoregressive policy. Directly running this file in command line will train an ARP model to generate binary mnist images.

   • The only hairy part of the code is the generate function, which is, in principle simple but has some engineering details.
   • Note, action decoder (in paper) are named as predictor in this file.
   • Here are my ongoing documentation.

7. We provide 2d-waypoints-real-robot.ipynb, which shows you how to get 2d waypoints or 2d Joint locations (which can be used as guidance for low-level actions), from URDF, camera parameters and joint positions of real robots.

Citation

In case this work is helpful for your research, please cite:

@misc{zhang2024arp,
      title={Autoregressive Action Sequence Learning for Robotic Manipulation}, 
      author={Xinyu Zhang, Yuhan Liu, Haonan Chang, Liam Schramm, and Abdeslam Boularias},
      year={2024},
      eprint={arXiv:2410.03132},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2410.03132}, 
}