Learning Dexterous In-Hand Manipulation with Multifingered Hands via Visuomotor Diffusion
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910857986834432 |
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| author | Koczy, Piotr Welle, Michael C. Kragic, Danica |
| author_facet | Koczy, Piotr Welle, Michael C. Kragic, Danica |
| contents | We present a framework for learning dexterous in-hand manipulation with multifingered hands using visuomotor diffusion policies. Our system enables complex in-hand manipulation tasks, such as unscrewing a bottle lid with one hand, by leveraging a fast and responsive teleoperation setup for the four-fingered Allegro Hand. We collect high-quality expert demonstrations using an augmented reality (AR) interface that tracks hand movements and applies inverse kinematics and motion retargeting for precise control. The AR headset provides real-time visualization, while gesture controls streamline teleoperation. To enhance policy learning, we introduce a novel demonstration outlier removal approach based on HDBSCAN clustering and the Global-Local Outlier Score from Hierarchies (GLOSH) algorithm, effectively filtering out low-quality demonstrations that could degrade performance. We evaluate our approach extensively in real-world settings and provide all experimental videos on the project website: https://dex-manip.github.io/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_02587 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Dexterous In-Hand Manipulation with Multifingered Hands via Visuomotor Diffusion Koczy, Piotr Welle, Michael C. Kragic, Danica Robotics We present a framework for learning dexterous in-hand manipulation with multifingered hands using visuomotor diffusion policies. Our system enables complex in-hand manipulation tasks, such as unscrewing a bottle lid with one hand, by leveraging a fast and responsive teleoperation setup for the four-fingered Allegro Hand. We collect high-quality expert demonstrations using an augmented reality (AR) interface that tracks hand movements and applies inverse kinematics and motion retargeting for precise control. The AR headset provides real-time visualization, while gesture controls streamline teleoperation. To enhance policy learning, we introduce a novel demonstration outlier removal approach based on HDBSCAN clustering and the Global-Local Outlier Score from Hierarchies (GLOSH) algorithm, effectively filtering out low-quality demonstrations that could degrade performance. We evaluate our approach extensively in real-world settings and provide all experimental videos on the project website: https://dex-manip.github.io/ |
| title | Learning Dexterous In-Hand Manipulation with Multifingered Hands via Visuomotor Diffusion |
| topic | Robotics |
| url | https://arxiv.org/abs/2503.02587 |