提出近似最优的接触丰富操作规划方法，让机械臂更高效自然地操作物体。
Approximately Optimal Global Planning for Contact-Rich SE(2) Manipulation on a Graph of Reachable Sets
- 离线构建可达集图，线上基于图规划全局优化路径
- 任务成本降低61%，250次测试成功率91%，单次查询低于1分钟
- 适合需要高效接触操作的机器人实际应用
若考虑人类操作，接触丰富操作（CRM）——即利用机械臂任意表面与物体接触——相比仅依赖末端执行器（如指尖）更具效率和自然性。然而，当前基于模型的CRM规划仍聚焦于可行性而非最优性，限制了其优势发挥。本文提出一种新范式，可计算近似最优的机械臂运动规划。该方法分为两阶段：离线阶段构建相互可达集图，每个集合包含从特定物体姿态和抓取起点可达的所有物体姿态；在线阶段在此图上进行规划，有效计算并序列化局部规划以实现全局优化。在一项具有挑战性的代表性接触丰富任务中，该方法优于领先规划器，任务成本降低61%。在250次查询中成功率高达91%，且保持亚分钟级查询时间，最终证明全局优化的接触丰富操作已具备实际应用可行性。
原文摘要 · Abstract (English)
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.
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