RoboManipBaselines: A Unified Framework for Imitation Learning in Robotic Manipulation across Real and Simulation Environments
Paper • 2509.17057 • Published
Spatial attention recurrent neural network (SARNN)
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 Sarnn MujocoUR5eCable --checkpoint ./checkpoint/Sarnn/<checkpoint_name>/policy_last.ckpt
Technical Details
For more information on the technical details, please see the following paper:
@INPROCEEDINGS{SARNN_ICRA2022,
author = {Ichiwara, Hideyuki and Ito, Hiroshi and Yamamoto, Kenjiro and Mori, Hiroki and Ogata, Tetsuya},
title = {Contact-Rich Manipulation of a Flexible Object based on Deep Predictive Learning using Vision and Tactility},
booktitle = {International Conference on Robotics and Automation},
year = {2022},
pages = {5375-5381},
doi = {10.1109/ICRA46639.2022.9811940}
}
RoboManipBaselines Paper
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