2024 IEEE 20th International Conference on Automation Science and Engineering (CASE) 20th IEEE International Conference on Automation Science and Engineering, CASE 2024. :1815-1822

Engineering and Technology, Electrical Engineering, Electronic Engineering, Information Engineering, Robotics and automation, Teknik, Elektroteknik och elektronik, Robotik och automation

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.