ForceMimic: Force-Centric Imitation Learning with Force-Motion Capture System for Contact-Rich Manipulation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909522028658688 |
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| author | Liu, Wenhai Wang, Junbo Wang, Yiming Wang, Weiming Lu, Cewu |
| author_facet | Liu, Wenhai Wang, Junbo Wang, Yiming Wang, Weiming Lu, Cewu |
| contents | 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-ofthe-art pure-vision-based imitation learning. Hardware, code, data and more results can be found on the project website at https://forcemimic.github.io. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_07554 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ForceMimic: Force-Centric Imitation Learning with Force-Motion Capture System for Contact-Rich Manipulation Liu, Wenhai Wang, Junbo Wang, Yiming Wang, Weiming Lu, Cewu Robotics 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-ofthe-art pure-vision-based imitation learning. Hardware, code, data and more results can be found on the project website at https://forcemimic.github.io. |
| title | ForceMimic: Force-Centric Imitation Learning with Force-Motion Capture System for Contact-Rich Manipulation |
| topic | Robotics |
| url | https://arxiv.org/abs/2410.07554 |