机器人学中的可适应恢复行为：一种用于故障管理的 Behavior Trees and Motion Generators(BTMG) 方法
Adaptable Recovery Behaviors in Robotics: A Behavior Trees and Motion Generators(BTMG) Approach for Failure Management
AI总结：
本文提出基于 BTMG 框架将恢复行为建模为可适应机器人技能，结合强化学习动态调整参数，以最少人工干预应对协作机器人各类故障，并在 peg-in-a-hole 任务和双臂 KUKA 机器人上验证了其能有效提升操作效率与任务成功率。
摘要：
在动态操作环境中，特别是在协作机器人领域，故障的不可避免性需要稳健且可适应的恢复策略。传统的自动恢复策略虽然对预定义场景有效，但通常缺乏即时任务管理和适应预期故障所需的灵活性。为了解决这一差距，我们提出了一种新方法，将恢复行为建模为可适应的机器人技能，利用 Behavior Trees and Motion Generators (BTMG) 框架进行策略表示。该方法的独特之处在于采用强化学习 (RL) 动态调整恢复行为参数，从而能够以最少的人工干预对各种故障场景做出定制化响应。我们通过在 peg-in-a-hole 任务中的一系列逐步挑战性的场景评估了我们的方法，证明了该方法在提高协作机器人环境中的操作效率和任务成功率方面的有效性。我们使用双臂 KUKA 机器人验证了我们的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
