让机器人用黑盒技能完成复杂任务，还能自动调整动作。
Task and Skill Planning: Hierarchical Robot Planning with Black-Box Skills
- 用可组合交互原语连接不同技能，生成衔接动作
- 实测在双臂和移动机械臂上成功完成多房间任务
- 适合有多种异构技能的机器人系统开发者
任务与运动规划（TAMP）是解决长周期机器人规划问题的经典方法。尽管传统TAMP假设每个任务级动作（即技能）可简化为运动规划，但近期工作开始将闭环控制器和学习型技能融入此类系统。本文提出任务与技能规划（TASP），将预训练的异构技能——包括学习型、力控型及黑盒策略——整合进分层规划框架，同时保留典型TAMP求解器的以物体为中心的失败推理能力。我们利用可组合交互原语（CIPs）生成连接连续技能的头尾运动计划，支持规划阶段优化与执行阶段调整。通过在双臂操作器和移动操作器上的真实世界实验验证，结果表明CIPs使不同机器人能够组合异构技能，解决复杂且长周期的任务，包括具有非单调结构的多房间移动操作任务。
原文摘要 · Abstract (English)
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.
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