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.

翻译：在动态操作环境中，尤其是在协作机器人领域，故障的必然性要求具备稳健且自适应的恢复策略。传统的自动化恢复策略虽然对预设场景有效，但往往缺乏应对即时任务管理和预期故障所需的自适应能力。针对这一不足，我们提出了一种新方法，将恢复行为建模为可自适应的机器人技能，并利用行为树与运动生成器（BTMG）框架进行策略表示。该方法的核心创新在于采用强化学习（RL）动态优化恢复行为参数，从而在最小化人工干预的前提下，实现对各类故障场景的定制化响应。我们通过一系列难度递增的插销入孔任务场景评估了该方法，验证了其在协作机器人场景中提升操作效率和任务成功率的有效性，并在双臂KUKA机器人上完成了实际验证。