Approximately Optimal Global Planning for Contact-Rich SE(2) Manipulation on a Graph of Reachable Sets
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914258172772352 |
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| author | Liu, Simin Zhao, Tong Graesdal, Bernhard Paus Werner, Peter Wang, Jiuguang Dolan, John Liu, Changliu Pang, Tao |
| author_facet | Liu, Simin Zhao, Tong Graesdal, Bernhard Paus Werner, Peter Wang, Jiuguang Dolan, John Liu, Changliu Pang, Tao |
| contents | If we consider human manipulation, it is clear that contact-rich manipulation (CRM)-the ability to use any surface of the manipulator to make contact with objects-can be far more efficient and natural than relying solely on end-effectors (i.e., fingertips). However, state-of-the-art model-based planners for CRM are still focused on feasibility rather than optimality, limiting their ability to fully exploit CRM's advantages. We introduce a new paradigm that computes approximately optimal manipulator plans. This approach has two phases. Offline, we construct a graph of mutual reachable sets, where each set contains all object orientations reachable from a starting object orientation and grasp. Online, we plan over this graph, effectively computing and sequencing local plans for globally optimized motion. On a challenging, representative contact-rich task, our approach outperforms a leading planner, reducing task cost by 61%. It also achieves a 91% success rate across 250 queries and maintains sub-minute query times, ultimately demonstrating that globally optimized contact-rich manipulation is now practical for real-world tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_10827 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Approximately Optimal Global Planning for Contact-Rich SE(2) Manipulation on a Graph of Reachable Sets Liu, Simin Zhao, Tong Graesdal, Bernhard Paus Werner, Peter Wang, Jiuguang Dolan, John Liu, Changliu Pang, Tao Robotics Artificial Intelligence Systems and Control If we consider human manipulation, it is clear that contact-rich manipulation (CRM)-the ability to use any surface of the manipulator to make contact with objects-can be far more efficient and natural than relying solely on end-effectors (i.e., fingertips). However, state-of-the-art model-based planners for CRM are still focused on feasibility rather than optimality, limiting their ability to fully exploit CRM's advantages. We introduce a new paradigm that computes approximately optimal manipulator plans. This approach has two phases. Offline, we construct a graph of mutual reachable sets, where each set contains all object orientations reachable from a starting object orientation and grasp. Online, we plan over this graph, effectively computing and sequencing local plans for globally optimized motion. On a challenging, representative contact-rich task, our approach outperforms a leading planner, reducing task cost by 61%. It also achieves a 91% success rate across 250 queries and maintains sub-minute query times, ultimately demonstrating that globally optimized contact-rich manipulation is now practical for real-world tasks. |
| title | Approximately Optimal Global Planning for Contact-Rich SE(2) Manipulation on a Graph of Reachable Sets |
| topic | Robotics Artificial Intelligence Systems and Control |
| url | https://arxiv.org/abs/2601.10827 |