תכנון משימה ומיומנות: תכנון רובוטי היררכי עם מיומנויות שחור-קופסה

תקציר מקורי באנגליתarXiv:2504.17901v3 Announce Type: replace-cross 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-ti