This paper introduces H-MaP, a hybrid sequential manipulation planner that addresses complex tasks requiring both sequential actions and dynamic contact mode switches. Our approach reduces configuration space dimensionality by decoupling object trajectory planning from manipulation planning through object-based waypoint generation, informed contact sampling, and optimization-based motion planning. This architecture enables handling of challenging scenarios involving tool use, auxiliary object manipulation, and bimanual coordination. Experimental results across seven diverse tasks demonstrate H-MaP's superior performance compared to existing methods, particularly in highly constrained environments where traditional approaches fail due to local minima or scalability issues. The planner's effectiveness is validated through both simulation and real-robot experiments.

翻译：本文介绍了H-MaP，一种用于解决需要序列化动作与动态接触模式切换的复杂任务的混合序列操作规划器。该方法通过基于对象的路径点生成、信息驱动的接触采样以及基于优化的运动规划，将物体轨迹规划与操作规划解耦，从而降低了构型空间的维度。该架构能够处理涉及工具使用、辅助物体操作和双手协调的复杂场景。在七项不同任务上的实验结果表明，与现有方法相比，H-MaP展现出更优越的性能，尤其在传统方法因局部极小值或可扩展性问题而失效的高度受限环境中表现突出。该规划器的有效性已通过仿真和真实机器人实验得到验证。