论文标题
通过增强学习，具有优化控制刚度的装配机器人
Assembly robots with optimized control stiffness through reinforcement learning
论文作者
论文摘要
机器人对任务自动化的需求增加。在一系列操作中进行多个接触过渡的接触率丰富的任务正在广泛研究以实现高精度。在这项研究中，我们提出了一种使用强化学习（RL）来实现机器人中高性能的方法，以执行需要与对象进行精确接触而不会造成损害的组装任务。提出的方法可确保在线生成刚度矩阵，有助于提高局部轨迹优化的性能。由于轨迹计划的简短采样时间，该方法具有快速响应的优势。该方法的有效性通过涉及两个接触量任务的实验验证。结果表明，所提出的方法可以在各种接触的操作中实现。演示视频显示了表演。 （https://youtu.be/gxscl7tp4-0）
There is an increased demand for task automation in robots. Contact-rich tasks, wherein multiple contact transitions occur in a series of operations, are extensively being studied to realize high accuracy. In this study, we propose a methodology that uses reinforcement learning (RL) to achieve high performance in robots for the execution of assembly tasks that require precise contact with objects without causing damage. The proposed method ensures the online generation of stiffness matrices that help improve the performance of local trajectory optimization. The method has an advantage of rapid response owing to short sampling time of the trajectory planning. The effectiveness of the method was verified via experiments involving two contact-rich tasks. The results indicate that the proposed method can be implemented in various contact-rich manipulations. A demonstration video shows the performance. (https://youtu.be/gxSCl7Tp4-0)
