DexViTac: Collecting Human Visuo-Tactile-Kinematic Demonstrations for Contact-Rich Dexterous Manipulation
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866908898120695808 |
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| author | Chen, Xitong Pan, Yifeng Li, Min Ding, Xiaotian |
| author_facet | Chen, Xitong Pan, Yifeng Li, Min Ding, Xiaotian |
| contents | Large-scale, high-quality multimodal demonstrations are essential for robot learning of contact-rich dexterous manipulation. While human-centric data collection systems lower the barrier to scaling, they struggle to capture the tactile information during physical interactions. Motivated by this, we present DexViTac, a portable, human-centric data collection system tailored for contact-rich dexterous manipulation. The system enables the high-fidelity acquisition of first-person vision, high-density tactile sensing, end-effector poses, and hand kinematics within unstructured, in-the-wild environments. Building upon this hardware, we propose a kinematics-grounded tactile representation learning algorithm that effectively resolves semantic ambiguities within tactile signals. Leveraging the efficiency of DexViTac, we construct a multimodal dataset comprising over 2,400 visuo-tactile-kinematic demonstrations. Experiments demonstrate that DexViTac achieves a collection efficiency exceeding 248 demonstrations per hour and remains robust against complex visual occlusions. Real-world deployment confirms that policies trained with the proposed dataset and learning strategy achieve an average success rate exceeding 85% across four challenging tasks. This performance significantly outperforms baseline methods, thereby validating the substantial improvement the system provides for learning contact-rich dexterous manipulation. Project page: https://xitong-c.github.io/DexViTac/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_17851 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DexViTac: Collecting Human Visuo-Tactile-Kinematic Demonstrations for Contact-Rich Dexterous Manipulation Chen, Xitong Pan, Yifeng Li, Min Ding, Xiaotian Robotics Large-scale, high-quality multimodal demonstrations are essential for robot learning of contact-rich dexterous manipulation. While human-centric data collection systems lower the barrier to scaling, they struggle to capture the tactile information during physical interactions. Motivated by this, we present DexViTac, a portable, human-centric data collection system tailored for contact-rich dexterous manipulation. The system enables the high-fidelity acquisition of first-person vision, high-density tactile sensing, end-effector poses, and hand kinematics within unstructured, in-the-wild environments. Building upon this hardware, we propose a kinematics-grounded tactile representation learning algorithm that effectively resolves semantic ambiguities within tactile signals. Leveraging the efficiency of DexViTac, we construct a multimodal dataset comprising over 2,400 visuo-tactile-kinematic demonstrations. Experiments demonstrate that DexViTac achieves a collection efficiency exceeding 248 demonstrations per hour and remains robust against complex visual occlusions. Real-world deployment confirms that policies trained with the proposed dataset and learning strategy achieve an average success rate exceeding 85% across four challenging tasks. This performance significantly outperforms baseline methods, thereby validating the substantial improvement the system provides for learning contact-rich dexterous manipulation. Project page: https://xitong-c.github.io/DexViTac/. |
| title | DexViTac: Collecting Human Visuo-Tactile-Kinematic Demonstrations for Contact-Rich Dexterous Manipulation |
| topic | Robotics |
| url | https://arxiv.org/abs/2603.17851 |