DexCatch: Learning to Catch Arbitrary Objects with Dexterous Hands
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866917751615913984 |
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| author | Lan, Fengbo Wang, Shengjie Zhang, Yunzhe Xu, Haotian Oseni, Oluwatosin Zhang, Ziye Gao, Yang Zhang, Tao |
| author_facet | Lan, Fengbo Wang, Shengjie Zhang, Yunzhe Xu, Haotian Oseni, Oluwatosin Zhang, Ziye Gao, Yang Zhang, Tao |
| contents | Achieving human-like dexterous manipulation remains a crucial area of research in robotics. Current research focuses on improving the success rate of pick-and-place tasks. Compared with pick-and-place, throwing-catching behavior has the potential to increase the speed of transporting objects to their destination. However, dynamic dexterous manipulation poses a major challenge for stable control due to a large number of dynamic contacts. In this paper, we propose a Learning-based framework for Throwing-Catching tasks using dexterous hands (LTC). Our method, LTC, achieves a 73\% success rate across 45 scenarios (diverse hand poses and objects), and the learned policies demonstrate strong zero-shot transfer performance on unseen objects. Additionally, in tasks where the object in hand faces sideways, an extremely unstable scenario due to the lack of support from the palm, all baselines fail, while our method still achieves a success rate of over 60\%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_08809 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | DexCatch: Learning to Catch Arbitrary Objects with Dexterous Hands Lan, Fengbo Wang, Shengjie Zhang, Yunzhe Xu, Haotian Oseni, Oluwatosin Zhang, Ziye Gao, Yang Zhang, Tao Robotics Artificial Intelligence Achieving human-like dexterous manipulation remains a crucial area of research in robotics. Current research focuses on improving the success rate of pick-and-place tasks. Compared with pick-and-place, throwing-catching behavior has the potential to increase the speed of transporting objects to their destination. However, dynamic dexterous manipulation poses a major challenge for stable control due to a large number of dynamic contacts. In this paper, we propose a Learning-based framework for Throwing-Catching tasks using dexterous hands (LTC). Our method, LTC, achieves a 73\% success rate across 45 scenarios (diverse hand poses and objects), and the learned policies demonstrate strong zero-shot transfer performance on unseen objects. Additionally, in tasks where the object in hand faces sideways, an extremely unstable scenario due to the lack of support from the palm, all baselines fail, while our method still achieves a success rate of over 60\%. |
| title | DexCatch: Learning to Catch Arbitrary Objects with Dexterous Hands |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2310.08809 |