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.

翻译：若考察人类操作行为，可明显发现接触丰富操作——即利用操作器任意表面与物体建立接触的能力——相比仅依赖末端执行器（如指尖）的方式，其效率与自然性显著更优。然而，当前基于模型的接触丰富操作规划方法仍聚焦于可行性而非最优性，这限制了其充分发挥接触丰富操作优势的能力。本文提出一种计算近似最优操作器规划的新范式。该方法包含两个阶段：离线阶段构建互达集合图，其中每个集合包含从起始物体姿态与抓取状态可达的所有物体朝向；在线阶段在该图上进行规划，通过有效计算并序列化局部规划来实现全局优化运动。在一项具有挑战性的典型接触丰富任务中，本方法优于主流规划器，任务成本降低61%。该方法在250次查询中达成91%的成功率，并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在实际任务中已具备可行性。