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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2507.17085 |
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| _version_ | 1866909974153658368 |
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| author | Jacob, Jayadeep Zhang, Wenzheng Warren, Houston Borges, Paulo Bandyopadhyay, Tirthankar Ramos, Fabio |
| author_facet | Jacob, Jayadeep Zhang, Wenzheng Warren, Houston Borges, Paulo Bandyopadhyay, Tirthankar Ramos, Fabio |
| contents | Manipulating clusters of deformable objects presents a substantial challenge with widespread applicability, but requires contact-rich whole-arm interactions. A potential solution must address the limited capacity for realistic model synthesis, high uncertainty in perception, and the lack of efficient spatial abstractions, among others. We propose a novel framework for learning model-free policies integrating two modalities: 3D point clouds and proprioceptive touch indicators, emphasising manipulation with full body contact awareness, going beyond traditional end-effector modes. Our reinforcement learning framework leverages a distributional state representation, aided by kernel mean embeddings, to achieve improved training efficiency and real-time inference. Furthermore, we propose a novel context-agnostic occlusion heuristic to clear deformables from a target region for exposure tasks. We deploy the framework in a power line clearance scenario and observe that the agent generates creative strategies leveraging multiple arm links for de-occlusion. Finally, we perform zero-shot sim-to-real policy transfer, allowing the arm to clear real branches with unknown occlusion patterns, unseen topology, and uncertain dynamics. Website: https://sites.google.com/view/dcmwap/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_17085 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deformable Cluster Manipulation via Whole-Arm Policy Learning Jacob, Jayadeep Zhang, Wenzheng Warren, Houston Borges, Paulo Bandyopadhyay, Tirthankar Ramos, Fabio Robotics Machine Learning Manipulating clusters of deformable objects presents a substantial challenge with widespread applicability, but requires contact-rich whole-arm interactions. A potential solution must address the limited capacity for realistic model synthesis, high uncertainty in perception, and the lack of efficient spatial abstractions, among others. We propose a novel framework for learning model-free policies integrating two modalities: 3D point clouds and proprioceptive touch indicators, emphasising manipulation with full body contact awareness, going beyond traditional end-effector modes. Our reinforcement learning framework leverages a distributional state representation, aided by kernel mean embeddings, to achieve improved training efficiency and real-time inference. Furthermore, we propose a novel context-agnostic occlusion heuristic to clear deformables from a target region for exposure tasks. We deploy the framework in a power line clearance scenario and observe that the agent generates creative strategies leveraging multiple arm links for de-occlusion. Finally, we perform zero-shot sim-to-real policy transfer, allowing the arm to clear real branches with unknown occlusion patterns, unseen topology, and uncertain dynamics. Website: https://sites.google.com/view/dcmwap/ |
| title | Deformable Cluster Manipulation via Whole-Arm Policy Learning |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2507.17085 |