DexTac: Learning Contact-aware Visuotactile Policies via Hand-by-hand Teaching
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914290009636864 |
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| author | Zhang, Xingyu Zhang, Chaofan Zhang, Boyue Peng, Zhinan Cui, Shaowei Wang, Shuo |
| author_facet | Zhang, Xingyu Zhang, Chaofan Zhang, Boyue Peng, Zhinan Cui, Shaowei Wang, Shuo |
| contents | For contact-intensive tasks, the ability to generate policies that produce comprehensive tactile-aware motions is essential. However, existing data collection and skill learning systems for dexterous manipulation often suffer from low-dimensional tactile information. To address this limitation, we propose DexTac, a visuo-tactile manipulation learning framework based on kinesthetic teaching. DexTac captures multi-dimensional tactile data-including contact force distributions and spatial contact regions-directly from human demonstrations. By integrating these rich tactile modalities into a policy network, the resulting contact-aware agent enables a dexterous hand to autonomously select and maintain optimal contact regions during complex interactions. We evaluate our framework on a challenging unimanual injection task. Experimental results demonstrate that DexTac achieves a 91.67% success rate. Notably, in high-precision scenarios involving small-scale syringes, our approach outperforms force-only baselines by 31.67%. These results underscore that learning multi-dimensional tactile priors from human demonstrations is critical for achieving robust, human-like dexterous manipulation in contact-rich environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_21474 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DexTac: Learning Contact-aware Visuotactile Policies via Hand-by-hand Teaching Zhang, Xingyu Zhang, Chaofan Zhang, Boyue Peng, Zhinan Cui, Shaowei Wang, Shuo Robotics For contact-intensive tasks, the ability to generate policies that produce comprehensive tactile-aware motions is essential. However, existing data collection and skill learning systems for dexterous manipulation often suffer from low-dimensional tactile information. To address this limitation, we propose DexTac, a visuo-tactile manipulation learning framework based on kinesthetic teaching. DexTac captures multi-dimensional tactile data-including contact force distributions and spatial contact regions-directly from human demonstrations. By integrating these rich tactile modalities into a policy network, the resulting contact-aware agent enables a dexterous hand to autonomously select and maintain optimal contact regions during complex interactions. We evaluate our framework on a challenging unimanual injection task. Experimental results demonstrate that DexTac achieves a 91.67% success rate. Notably, in high-precision scenarios involving small-scale syringes, our approach outperforms force-only baselines by 31.67%. These results underscore that learning multi-dimensional tactile priors from human demonstrations is critical for achieving robust, human-like dexterous manipulation in contact-rich environments. |
| title | DexTac: Learning Contact-aware Visuotactile Policies via Hand-by-hand Teaching |
| topic | Robotics |
| url | https://arxiv.org/abs/2601.21474 |