A Surprisingly Efficient Representation for Multi-Finger Grasping
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929449626238976 |
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| author | Yan, Hengxu Fang, Hao-Shu Lu, Cewu |
| author_facet | Yan, Hengxu Fang, Hao-Shu Lu, Cewu |
| contents | The problem of grasping objects using a multi-finger hand has received significant attention in recent years. However, it remains challenging to handle a large number of unfamiliar objects in real and cluttered environments. In this work, we propose a representation that can be effectively mapped to the multi-finger grasp space. Based on this representation, we develop a simple decision model that generates accurate grasp quality scores for different multi-finger grasp poses using only hundreds to thousands of training samples. We demonstrate that our representation performs well on a real robot and achieves a success rate of 78.64% after training with only 500 real-world grasp attempts and 87% with 4500 grasp attempts. Additionally, we achieve a success rate of 84.51% in a dynamic human-robot handover scenario using a multi-finger hand. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_02455 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Surprisingly Efficient Representation for Multi-Finger Grasping Yan, Hengxu Fang, Hao-Shu Lu, Cewu Robotics The problem of grasping objects using a multi-finger hand has received significant attention in recent years. However, it remains challenging to handle a large number of unfamiliar objects in real and cluttered environments. In this work, we propose a representation that can be effectively mapped to the multi-finger grasp space. Based on this representation, we develop a simple decision model that generates accurate grasp quality scores for different multi-finger grasp poses using only hundreds to thousands of training samples. We demonstrate that our representation performs well on a real robot and achieves a success rate of 78.64% after training with only 500 real-world grasp attempts and 87% with 4500 grasp attempts. Additionally, we achieve a success rate of 84.51% in a dynamic human-robot handover scenario using a multi-finger hand. |
| title | A Surprisingly Efficient Representation for Multi-Finger Grasping |
| topic | Robotics |
| url | https://arxiv.org/abs/2408.02455 |