Force-Centric Imitation Learning with Force-Motion Capture System for Contact-Rich Manipulation
<|reference_start|>Force-Centric Imitation Learning with Force-Motion Capture System for Contact-Rich Manipulation: In most contact-rich manipulation tasks, humans apply time-varying forces to the target object, compensating for inaccuracies in the vision-guided hand trajectory. However, current robot learning algorithms primarily focus on trajectory-based policy, with limited attention given to learning force-related skills. To address this limitation, we introduce ForceMimic, a force-centric robot learning system, providing a natural, force-aware and robot-free robotic demonstration collection system, along with a hybrid force-motion imitation learning algorithm for robust contact-rich manipulation. Using the proposed ForceCapture system, an operator can peel a zucchini in 5 minutes, while force-feedback teleoperation takes over 13 minutes and struggles with task completion. With the collected data, we propose HybridIL to train a force-centric imitation learning model, equipped with hybrid force-position control primitive to fit the predicted wrench-position parameters during robot execution. Experiments demonstrate that our approach enables the model to learn a more robust policy under the contact-rich task of vegetable peeling, increasing the success rates by 54.5% relatively compared to state-of-the-art pure-vision-based imitation learning. Hardware, code, data and more results would be open-sourced on the project website at https://forcemimic.github.io.<|reference_end|>
@article{liu2024forcemimic:,
    title={ForceMimic: Force-Centric Imitation Learning with Force-Motion Capture
  System for Contact-Rich Manipulation},
    author={Wenhai Liu, Junbo Wang, Yiming Wang, Weiming Wang, Cewu Lu},
    journal={arXiv preprint arXiv:2410.07554},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.07554},
    primaryClass={cs.RO}
}