Beyond Task and Motion Planning: Hierarchical Robot Planning with General-Purpose Policies
This addresses the problem of enabling robots to combine motion planning with general-purpose skills for complex tasks, representing an incremental advancement over traditional task and motion planning.
The paper tackles the challenge of integrating both kinematic skills and closed-loop motor controllers in robot planning, proposing a method using Composable Interaction Primitives (CIPs) to enable hierarchical planning with diverse pre-learned skills, with ongoing experiments in real-world scenarios.
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. In this work, we address the challenge of planning with both kinematic skills and closed-loop motor controllers that go beyond kinematic considerations. We propose a novel method that integrates these controllers into motion planning using Composable Interaction Primitives (CIPs), enabling the use of diverse, non-composable pre-learned skills in hierarchical robot planning. Toward validating our Task and Skill Planning (TASP) approach, we describe ongoing robot experiments in real-world scenarios designed to demonstrate how CIPs can allow a mobile manipulator robot to effectively combine motion planning with general-purpose skills to accomplish complex tasks.