Task and motion planning is a well-established approach for solving long-horizon robot planning problems. However, traditional methods assume that each task-level robot action, or skill, can be reduced to kinematic motion planning. We address the challenge of combining motion planning with closed-loop motor controllers that go beyond mere kinematic considerations. We propose a novel framework that integrates these policies into motion planning using Composable Interaction Primitives (CIPs), enabling the use of diverse, non-composable pre-learned skills in hierarchical robot planning. We validate our Task and Skill Planning (TASP) approach through real-world experiments on a bimanual manipulator and a mobile manipulator, demonstrating that CIPs allow diverse robots to combine motion planning with general-purpose skills to solve complex, long-horizon tasks.
@article{arxiv.2504.17901,
title = {Beyond Task and Motion Planning: Hierarchical Robot Planning with General-Purpose Skills},
author = {Benned Hedegaard and Yichen Wei and Ahmed Jaafar and Stefanie Tellex and George Konidaris and Naman Shah},
journal= {arXiv preprint arXiv:2504.17901},
year = {2026}
}