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arXiv · 2504.17901

Task and Skill Planning: Hierarchical Robot Planning with Black-Box Skills

Benned Hedegaard·Yichen Wei·Ziyi Yang·Ahmed Jaafar·Stefanie Tellex·George Konidaris·Naman Shah
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Abstract

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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Benned Hedegaard, Yichen Wei, Ziyi Yang, Ahmed Jaafar, Stefanie Tellex, George Konidaris, Naman Shah. 2025-04-24. Task and Skill Planning: Hierarchical Robot Planning with Black-Box Skills. https://arxiv.org/abs/2504.17901

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