Vi-TacMan: Articulated Object Manipulation via Vision and Touch
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915907354230784 |
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| author | Cui, Leiyao Zhao, Zihang Xie, Sirui Zhang, Wenhuan Han, Zhi Zhu, Yixin |
| author_facet | Cui, Leiyao Zhao, Zihang Xie, Sirui Zhang, Wenhuan Han, Zhi Zhu, Yixin |
| contents | Autonomous manipulation of articulated objects remains a fundamental challenge for robots in human environments. Vision-based methods can infer hidden kinematics but can yield imprecise estimates on unfamiliar objects. Tactile approaches achieve robust control through contact feedback but require accurate initialization. This suggests a natural synergy: vision for global guidance, touch for local precision. Yet no framework systematically exploits this complementarity for generalized articulated manipulation. Here we present Vi-TacMan, which uses vision to propose grasps and coarse directions that seed a tactile controller for precise execution. By incorporating surface normals as geometric priors and modeling directions via von Mises-Fisher distributions, our approach achieves significant gains over baselines (all p<0.0001). Critically, manipulation succeeds without explicit kinematic models -- the tactile controller refines coarse visual estimates through real-time contact regulation. Tests on more than 50,000 simulated and diverse real-world objects confirm robust cross-category generalization. This work establishes that coarse visual cues suffice for reliable manipulation when coupled with tactile feedback, offering a scalable paradigm for autonomous systems in unstructured environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06339 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Vi-TacMan: Articulated Object Manipulation via Vision and Touch Cui, Leiyao Zhao, Zihang Xie, Sirui Zhang, Wenhuan Han, Zhi Zhu, Yixin Robotics Autonomous manipulation of articulated objects remains a fundamental challenge for robots in human environments. Vision-based methods can infer hidden kinematics but can yield imprecise estimates on unfamiliar objects. Tactile approaches achieve robust control through contact feedback but require accurate initialization. This suggests a natural synergy: vision for global guidance, touch for local precision. Yet no framework systematically exploits this complementarity for generalized articulated manipulation. Here we present Vi-TacMan, which uses vision to propose grasps and coarse directions that seed a tactile controller for precise execution. By incorporating surface normals as geometric priors and modeling directions via von Mises-Fisher distributions, our approach achieves significant gains over baselines (all p<0.0001). Critically, manipulation succeeds without explicit kinematic models -- the tactile controller refines coarse visual estimates through real-time contact regulation. Tests on more than 50,000 simulated and diverse real-world objects confirm robust cross-category generalization. This work establishes that coarse visual cues suffice for reliable manipulation when coupled with tactile feedback, offering a scalable paradigm for autonomous systems in unstructured environments. |
| title | Vi-TacMan: Articulated Object Manipulation via Vision and Touch |
| topic | Robotics |
| url | https://arxiv.org/abs/2510.06339 |