ContactSDF: Signed Distance Functions as Multi-Contact Models for Dexterous Manipulation
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866909544156758016 |
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| author | Yang, Wen Jin, Wanxin |
| author_facet | Yang, Wen Jin, Wanxin |
| contents | In this paper, we propose ContactSDF, a method that uses signed distance functions (SDFs) to approximate multi-contact models, including both collision detection and time-stepping routines. ContactSDF first establishes an SDF using the supporting plane representation of an object for collision detection, and then uses the generated contact dual cones to build a second SDF for time-stepping prediction of the next state. Those two SDFs create a differentiable and closed-form multi-contact dynamic model for state prediction, enabling efficient model learning and optimization for contact-rich manipulation. We perform extensive simulation experiments to show the effectiveness of ContactSDF for model learning and real-time control of dexterous manipulation. We further evaluate the ContactSDF on a hardware Allegro hand for on-palm reorientation tasks. Results show with around 2 minutes of learning on hardware, the ContactSDF achieves high-quality dexterous manipulation at a frequency of 30-60Hz. Project page https://yangwen-1102.github.io/contactsdf.github.io/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_09612 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ContactSDF: Signed Distance Functions as Multi-Contact Models for Dexterous Manipulation Yang, Wen Jin, Wanxin Robotics In this paper, we propose ContactSDF, a method that uses signed distance functions (SDFs) to approximate multi-contact models, including both collision detection and time-stepping routines. ContactSDF first establishes an SDF using the supporting plane representation of an object for collision detection, and then uses the generated contact dual cones to build a second SDF for time-stepping prediction of the next state. Those two SDFs create a differentiable and closed-form multi-contact dynamic model for state prediction, enabling efficient model learning and optimization for contact-rich manipulation. We perform extensive simulation experiments to show the effectiveness of ContactSDF for model learning and real-time control of dexterous manipulation. We further evaluate the ContactSDF on a hardware Allegro hand for on-palm reorientation tasks. Results show with around 2 minutes of learning on hardware, the ContactSDF achieves high-quality dexterous manipulation at a frequency of 30-60Hz. Project page https://yangwen-1102.github.io/contactsdf.github.io/ |
| title | ContactSDF: Signed Distance Functions as Multi-Contact Models for Dexterous Manipulation |
| topic | Robotics |
| url | https://arxiv.org/abs/2408.09612 |