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，失败的必然性要求具备鲁棒且适应性强的恢复策略。传统的自动化恢复策略虽能有效处理预设场景，但在应对即时任务管理与意外失败时往往缺乏必要灵活性。针对这一不足，我们提出一种将恢复行为建模为可适应机器人技能的新方法，利用行为树与运动生成器（BTMG）框架进行策略表征。该方法创新性地采用强化学习（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失败场景的定制化响应。我们通过一系列难度递增的插销入孔任务场景评估该方法，证明了其在提升协作机器人操作效率与任务成功率方面的有效性。最终采用双机械臂KUKA机器人完成了方法验证。