Adaptable Recovery Behaviors in Robotics: A Behavior Trees and Motion Generators(BTMG) Approach for Failure Management
This addresses the need for robust and adaptable failure management in dynamic collaborative robotics environments, representing an incremental improvement over traditional predefined strategies.
The paper tackles the problem of inflexible automated recovery strategies in collaborative robotics by proposing a novel approach that models recovery behaviors as adaptable robotic skills using the BTMG framework with reinforcement learning to refine parameters dynamically. The method was validated on a dual-arm KUKA robot in peg-in-a-hole tasks, showing enhanced operational efficiency and task success rates.
In dynamic operational environments, particularly in collaborative robotics, the inevitability of failures necessitates robust and adaptable recovery strategies. Traditional automated recovery strategies, while effective for predefined scenarios, often lack the flexibility required for on-the-fly task management and adaptation to expected failures. Addressing this gap, we propose a novel approach that models recovery behaviors as adaptable robotic skills, leveraging the Behavior Trees and Motion Generators~(BTMG) framework for policy representation. This approach distinguishes itself by employing reinforcement learning~(RL) to dynamically refine recovery behavior parameters, enabling a tailored response to a wide array of failure scenarios with minimal human intervention. We assess our methodology through a series of progressively challenging scenarios within a peg-in-a-hole task, demonstrating the approach's effectiveness in enhancing operational efficiency and task success rates in collaborative robotics settings. We validate our approach using a dual-arm KUKA robot.