Recover, Discover, Plan: Learning Skills and Concepts from Robot Failures
ReSYNC introduces Recovery-Driven Synthesis of Relational Concepts to help robots avoid future failures. It uses an incremental dual-learning process: first, reinforcement learning learns specific recovery skills from failures, and second, concept learning discovers new relational predicates to generalize these skills. This allows the robot to convert local recoveries into global failure avoidance.