RoboManipBaselines: A Unified Framework for Imitation Learning in Robotic Manipulation across Real and Simulation Environments
Paper • 2509.17057 • Published
Action Chunking with Transformers (ACT)
The model was trained with the MujocoUR5eCable dataset.
Install
See GitHub for installation.
Policy rollout
Run a trained policy:
# Go to the top directory of this repository
$ cd robo_manip_baselines
$ python ./bin/Rollout.py Act MujocoUR5eCable --checkpoint ./checkpoint/Act/<checkpoint_name>/policy_last.ckpt
Technical Details
For more information on the technical details, please see the following paper:
@INPROCEEDINGS{ACT_RSS23,
author = {Tony Z. Zhao and Vikash Kumar and Sergey Levine and Chelsea Finn},
title = {Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware},
booktitle = {Proceedings of Robotics: Science and Systems},
year = {2023},
month = {July},
doi = {10.15607/RSS.2023.XIX.016}
}
RoboManipBaselines Paper
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