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We describe a task and motion planning architecture for highly dynamicsystems that combines a domain-independent sampling-based deliberative planningalgorithm with a global reactive planner. We leverage the recent development ofa reactive, vector field planner that provides guarantees of reachability tolarge regions of the environment even in the face of unknown or unforeseenobstacles. The reachability guarantees can be formalized using contracts thatallow a deliberative planner to reason purely in terms of those contracts andsynthesize a plan by choosing a sequence of reactive behaviors and their targetconfigurations, without evaluating specific motion plans between targets. Thisreduces both the search depth at which plans will be found, and the number ofsamples required to ensure a plan exists, while crucially preservingcorrectness guarantees. The result is reduced computational cost ofsynthesizing plans, and increased robustness of generated plans to actuatornoise, model misspecification, or unknown obstacles. Simulation studies showthat our hierarchical planning and execution architecture can solve complexnavigation and rearrangement tasks, even when faced with narrow passageways orincomplete world information.

Technical Report: A Hierarchical Deliberative-Reactive System Architecture for Task and Motion Planning in Partially Known Environments