Adaptable Recovery Behaviors for Robust Failure Management in Collaborative Robotics

This paper proposes a novel approach that models recovery behaviors as adaptable robotic skills, leveraging the Behavior Trees and Motion Generators (BTMG) framework and reinforcement learning to dynamically refine recovery behavior parameters. This enables robots to effectively handle a wide range of failure scenarios with minimal human intervention, enhancing operational efficiency and task success rates in collaborative robotics settings.