Learning Robotic Rotational Manipulation Skill from Bilateral Teleoperation

Joong-Ku Lee, J. Ryu
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Abstract

Bilateral teleoperation is an efficient and powerful solution for conducting manipulation tasks through the robot in remote environments. However, performing repetitive manipulation tasks through bilateral teleoperation induces a heavy human workload. The typical repetitive and difficult task in a real teleoperation scenario is the rotational manipulation task. Therefore, we propose a framework to learn the skill of conducting rotational manipulation tasks from a single human demonstration through bilateral teleoperation. We have experienced that the existing Cartesian orientation-based trajectory learning method could not properly encode and reproduce the rotational trajectory. Therefore, a method that utilizes task parameters to encode the trajectory is applied to the framework. Moreover, the rotational manipulation task cannot be successfully performed without considering physical interaction, even if there exists only a very small estimation error in the goal pose. Thus, we suggest a method to learn and utilize physical interaction from the demonstration. The experimental result on simulation and real robot conducting vial capping task shows that the proposed framework can learn and reproduce human skill of performing rotational manipulation task even with estimation error.
从双侧遥操作学习机器人旋转操作技巧
双向遥操作是机器人在远程环境下完成操作任务的一种有效而有力的解决方案。然而,通过双侧远程操作执行重复性操作任务会引起繁重的人力工作量。在实际远程操作场景中,典型的重复性和高难度任务是旋转操作任务。因此,我们提出了一个框架,通过双边远程操作,从单个人类演示中学习进行旋转操作任务的技能。现有的基于笛卡尔方向的轨迹学习方法不能很好地编码和再现旋转轨迹。因此,在框架中应用了利用任务参数对轨迹进行编码的方法。此外,如果不考虑物理交互,即使目标位姿只有很小的估计误差,旋转操作任务也无法成功执行。因此,我们提出了一种从演示中学习和利用物理交互的方法。在仿真机器人和真实机器人进行瓶子封盖任务的实验结果表明,即使存在估计误差,该框架也能学习和再现人类执行旋转操作任务的技能。
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