超越任务与运动规划：基于通用技能的分层机器人规划
Task and Skill Planning: Hierarchical Robot Planning with Black-Box Skills
AI总结：
本文提出了一种基于通用技能的分层机器人规划框架，通过可组合交互原语整合运动规划与闭合回路控制器，实现在复杂长期任务中的高效执行。
摘要：
任务与运动规划是解决长期机器人规划问题的成熟方法。然而，传统方法假设每个任务级的机器人动作或技能可以简化为运动学运动规划。我们面对将运动规划与具有闭合回路电机控制器相结合的挑战，这些控制器超越了单纯的运动学考虑。我们提出了一种新的框架，通过可组合交互原语（CIPs）将这些策略整合到运动规划中，使机器人规划能够利用多样且非可组合的预学习技能。我们通过在双臂机械臂和移动机械臂上的现实世界实验验证了我们的任务与技能规划（TASP）方法，证明CIPs使不同机器人能够结合运动规划与通用技能来解决复杂且长期的任务。
英文摘要：
Task and motion planning (TAMP) is a well-established approach for solving long-horizon robot planning problems. Although TAMP methods have historically assumed that each task-level robot action, or skill, can be reduced to kinematic motion planning, recent work has explored integrating closed-loop controllers and learned skills into TAMP-style systems. Our approach integrates pre-existing, heterogeneous robot skills--including learned, force-controlled, and black-box policies--into a hierarchical planner while preserving the object-centric failure reasoning of typical TAMP solvers. We leverage Composable Interaction Primitives (CIPs) to synthesize head and tail motion plans bridging consecutive skills, facilitating both planning-time refinement and execution-time adjustment. We validate our Task and Skill Planning (TASP) approach through real-world experiments on a bimanual manipulator and a mobile manipulator, demonstrating that CIPs enable diverse robots to combine heterogeneous skills to solve complex, long-horizon tasks, including multi-room mobile manipulation problems with non-monotonic task structure.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
