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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.17668 |
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| _version_ | 1866914773468184576 |
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| author | Lerner, Osher Tam, Zachary Equi, Michael |
| author_facet | Lerner, Osher Tam, Zachary Equi, Michael |
| contents | Precise object manipulation and placement is a common problem for household robots, surgery robots, and robots working on in-situ construction. Prior work using computer vision, depth sensors, and reinforcement learning lacks the ability to reactively recover from planning errors, execution errors, or sensor noise. This work introduces a method that uses force-torque sensing to robustly place objects in stable poses, even in adversarial environments. On 46 trials, our method finds success rates of 100% for basic stacking, and 17% for cases requiring adjustment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_17668 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Precise Object Placement Using Force-Torque Feedback Lerner, Osher Tam, Zachary Equi, Michael Robotics Precise object manipulation and placement is a common problem for household robots, surgery robots, and robots working on in-situ construction. Prior work using computer vision, depth sensors, and reinforcement learning lacks the ability to reactively recover from planning errors, execution errors, or sensor noise. This work introduces a method that uses force-torque sensing to robustly place objects in stable poses, even in adversarial environments. On 46 trials, our method finds success rates of 100% for basic stacking, and 17% for cases requiring adjustment. |
| title | Precise Object Placement Using Force-Torque Feedback |
| topic | Robotics |
| url | https://arxiv.org/abs/2404.17668 |