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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2408.06734 |
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| _version_ | 1866909286155681792 |
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| author | Li, Wanze Su, Wan Chirikjian, Gregory S. |
| author_facet | Li, Wanze Su, Wan Chirikjian, Gregory S. |
| contents | This paper proposes a novel learning-free three-stage method that predicts grasping poses, enabling robots to pick up and transfer previously unseen objects. Our method first identifies potential structures that can afford the action of hanging by analyzing the hanging mechanics and geometric properties. Then 6D poses are detected for a parallel gripper retrofitted with an extending bar, which when closed forms loops to hook each hangable structure. Finally, an evaluation policy qualities and rank grasp candidates for execution attempts. Compared to the traditional physical model-based and deep learning-based methods, our approach is closer to the human natural action of grasping unknown objects. And it also eliminates the need for a vast amount of training data. To evaluate the effectiveness of the proposed method, we conducted experiments with a real robot. Experimental results indicate that the grasping accuracy and stability are significantly higher than the state-of-the-art learning-based method, especially for thin and flat objects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_06734 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Grasping by Hanging: a Learning-Free Grasping Detection Method for Previously Unseen Objects Li, Wanze Su, Wan Chirikjian, Gregory S. Robotics This paper proposes a novel learning-free three-stage method that predicts grasping poses, enabling robots to pick up and transfer previously unseen objects. Our method first identifies potential structures that can afford the action of hanging by analyzing the hanging mechanics and geometric properties. Then 6D poses are detected for a parallel gripper retrofitted with an extending bar, which when closed forms loops to hook each hangable structure. Finally, an evaluation policy qualities and rank grasp candidates for execution attempts. Compared to the traditional physical model-based and deep learning-based methods, our approach is closer to the human natural action of grasping unknown objects. And it also eliminates the need for a vast amount of training data. To evaluate the effectiveness of the proposed method, we conducted experiments with a real robot. Experimental results indicate that the grasping accuracy and stability are significantly higher than the state-of-the-art learning-based method, especially for thin and flat objects. |
| title | Grasping by Hanging: a Learning-Free Grasping Detection Method for Previously Unseen Objects |
| topic | Robotics |
| url | https://arxiv.org/abs/2408.06734 |