Many manipulation tasks use instances of a set of common motions, such as a twisting motion for tightening or loosening a valve. However, different instances of the same motion often require different environmental parameters (e.g. force/torque level), and thus different manipulation strategies to successfully complete; for example, grasping a valve handle from the side rather than head-on to increase applied torque. Humans can intuitively adapt their manipulation strategy to best suit such problems, but representing and implementing such behaviors for robots remains an open question. We present a behavior tree-based approach for adaptive manipulation, wherein the robot can reactively select from and switch between a discrete set of manipulation strategies during task execution. Furthermore, our approach allows the robot to learn from past attempts to optimize performance, for example learning the optimal strategy for different task instances. Our approach also allows the robot to preempt task failure and either change to a more feasible strategy or safely exit the task before catastrophic failure occurs. We propose a simple behavior tree design for general adaptive robot behavior and apply it in the context of industrial manipulation. The adaptive behavior outperformed all baseline behaviors that only used a single manipulation strategy, markedly reducing the number of attempts and overall time taken to complete the example tasks. Our results demonstrate potential for improved robustness and efficiency in task completion, reducing dependency on human supervision and intervention.

翻译：许多操作任务利用一组常见运动模式的实例，例如用于拧紧或松开阀门的扭转运动。然而，同一运动模式的不同实例通常需要不同的环境参数（例如力/扭矩水平），因而需要采用不同的操作策略才能成功完成；例如，从侧面而非正面抓握阀门手柄以增加施加的扭矩。人类能够凭直觉调整操作策略以最优适应此类问题，但如何为机器人表示并实现此类行为仍是一个开放性问题。本文提出一种基于行为树的自适应操作方法，使机器人能够在任务执行过程中从一组离散的操作策略中反应性地选择并切换。此外，该方法使机器人能够从过往尝试中学习以优化性能，例如学习针对不同任务实例的最优策略。该方法还允许机器人预判任务失败风险，并切换至更可行的策略或在发生灾难性故障前安全中止任务。我们提出了一种适用于通用自适应机器人行为的简洁行为树设计方案，并将其应用于工业操作场景。实验表明，自适应行为在所有仅使用单一操作策略的基线行为中表现最优，显著减少了示例任务的尝试次数与总体完成时间。我们的研究结果证明了该方法在提升任务完成鲁棒性与效率方面的潜力，能够降低对人类监督与干预的依赖。