Failure-Aware Bimanual Teleoperation via Conservative Value Guided Assistance
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915767228825600 |
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| author | Zhou, Peng Li, Zhongxuan Wu, Jinsong Qi, Jiaming Hu, Jun Navarro-Alarcon, David Pan, Jia Xie, Lihua Zhang, Shiyao Zhang, Zeqing |
| author_facet | Zhou, Peng Li, Zhongxuan Wu, Jinsong Qi, Jiaming Hu, Jun Navarro-Alarcon, David Pan, Jia Xie, Lihua Zhang, Shiyao Zhang, Zeqing |
| contents | Teleoperation of high-precision manipulation is con-strained by tight success tolerances and complex contact dy-namics, which make impending failures difficult for human operators to anticipate under partial observability. This paper proposes a value-guided, failure-aware framework for bimanual teleoperation that provides compliant haptic assistance while pre-serving continuous human authority. The framework is trained entirely from heterogeneous offline teleoperation data containing both successful and failed executions. Task feasibility is mod-eled as a conservative success score learned via Conservative Value Learning, yielding a risk-sensitive estimate that remains reliable under distribution shift. During online operation, the learned success score regulates the level of assistance, while a learned actor provides a corrective motion direction. Both are integrated through a joint-space impedance interface on the master side, yielding continuous guidance that steers the operator away from failure-prone actions without overriding intent. Experimental results on contact-rich manipulation tasks demonstrate improved task success rates and reduced operator workload compared to conventional teleoperation and shared-autonomy baselines, indicating that conservative value learning provides an effective mechanism for embedding failure awareness into bilateral teleoperation. Experimental videos are available at https://www.youtube.com/watch?v=XDTsvzEkDRE |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_01092 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Failure-Aware Bimanual Teleoperation via Conservative Value Guided Assistance Zhou, Peng Li, Zhongxuan Wu, Jinsong Qi, Jiaming Hu, Jun Navarro-Alarcon, David Pan, Jia Xie, Lihua Zhang, Shiyao Zhang, Zeqing Robotics Teleoperation of high-precision manipulation is con-strained by tight success tolerances and complex contact dy-namics, which make impending failures difficult for human operators to anticipate under partial observability. This paper proposes a value-guided, failure-aware framework for bimanual teleoperation that provides compliant haptic assistance while pre-serving continuous human authority. The framework is trained entirely from heterogeneous offline teleoperation data containing both successful and failed executions. Task feasibility is mod-eled as a conservative success score learned via Conservative Value Learning, yielding a risk-sensitive estimate that remains reliable under distribution shift. During online operation, the learned success score regulates the level of assistance, while a learned actor provides a corrective motion direction. Both are integrated through a joint-space impedance interface on the master side, yielding continuous guidance that steers the operator away from failure-prone actions without overriding intent. Experimental results on contact-rich manipulation tasks demonstrate improved task success rates and reduced operator workload compared to conventional teleoperation and shared-autonomy baselines, indicating that conservative value learning provides an effective mechanism for embedding failure awareness into bilateral teleoperation. Experimental videos are available at https://www.youtube.com/watch?v=XDTsvzEkDRE |
| title | Failure-Aware Bimanual Teleoperation via Conservative Value Guided Assistance |
| topic | Robotics |
| url | https://arxiv.org/abs/2602.01092 |