Is Linear Feedback on Smoothed Dynamics Sufficient for Stabilizing Contact-Rich Plans?
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866918153365225472 |
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| author | Shirai, Yuki Zhao, Tong Suh, H. J. Terry Zhu, Huaijiang Ni, Xinpei Wang, Jiuguang Simchowitz, Max Pang, Tao |
| author_facet | Shirai, Yuki Zhao, Tong Suh, H. J. Terry Zhu, Huaijiang Ni, Xinpei Wang, Jiuguang Simchowitz, Max Pang, Tao |
| contents | Designing planners and controllers for contact-rich manipulation is extremely challenging as contact violates the smoothness conditions that many gradient-based controller synthesis tools assume. Contact smoothing approximates a non-smooth system with a smooth one, allowing one to use these synthesis tools more effectively. However, applying classical control synthesis methods to smoothed contact dynamics remains relatively under-explored. This paper analyzes the efficacy of linear controller synthesis using differential simulators based on contact smoothing. We introduce natural baselines for leveraging contact smoothing to compute (a) open-loop plans robust to uncertain conditions and/or dynamics, and (b) feedback gains to stabilize around open-loop plans. Using robotic bimanual whole-body manipulation as a testbed, we perform extensive empirical experiments on over 300 trajectories and analyze why LQR seems insufficient for stabilizing contact-rich plans. The video summarizing this paper and hardware experiments is found here: https://youtu.be/HLaKi6qbwQg?si=_zCAmBBD6rGSitm9. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_06542 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Is Linear Feedback on Smoothed Dynamics Sufficient for Stabilizing Contact-Rich Plans? Shirai, Yuki Zhao, Tong Suh, H. J. Terry Zhu, Huaijiang Ni, Xinpei Wang, Jiuguang Simchowitz, Max Pang, Tao Robotics Artificial Intelligence Systems and Control Designing planners and controllers for contact-rich manipulation is extremely challenging as contact violates the smoothness conditions that many gradient-based controller synthesis tools assume. Contact smoothing approximates a non-smooth system with a smooth one, allowing one to use these synthesis tools more effectively. However, applying classical control synthesis methods to smoothed contact dynamics remains relatively under-explored. This paper analyzes the efficacy of linear controller synthesis using differential simulators based on contact smoothing. We introduce natural baselines for leveraging contact smoothing to compute (a) open-loop plans robust to uncertain conditions and/or dynamics, and (b) feedback gains to stabilize around open-loop plans. Using robotic bimanual whole-body manipulation as a testbed, we perform extensive empirical experiments on over 300 trajectories and analyze why LQR seems insufficient for stabilizing contact-rich plans. The video summarizing this paper and hardware experiments is found here: https://youtu.be/HLaKi6qbwQg?si=_zCAmBBD6rGSitm9. |
| title | Is Linear Feedback on Smoothed Dynamics Sufficient for Stabilizing Contact-Rich Plans? |
| topic | Robotics Artificial Intelligence Systems and Control |
| url | https://arxiv.org/abs/2411.06542 |