Deep imitation learning is promising for robot manipulation because it only requires demonstration samples. In this study, deep imitation learning is applied to tasks that require force feedback. However, existing demonstration methods have deficiencies; bilateral teleoperation requires a complex control scheme and is expensive, and kinesthetic teaching suffers from visual distractions from human intervention. This research proposes a new master-to-robot (M2R) policy transfer system that does not require robots for teaching force feedback-based manipulation tasks. The human directly demonstrates a task using a controller. This controller resembles the kinematic parameters of the robot arm and uses the same end-effector with force/torque (F/T) sensors to measure the force feedback. Using this controller, the operator can feel force feedback without a bilateral system. The proposed method can overcome domain gaps between the master and robot using gaze-based imitation learning and a simple calibration method. Furthermore, a Transformer is applied to infer policy from F/T sensory input. The proposed system was evaluated on a bottle-cap-opening task that requires force feedback.

翻译：深度模仿学习因仅需示范样本而在机器人操作领域前景广阔。本研究将深度模仿学习应用于需要力反馈的任务中。然而，现有示范方法存在不足：双边遥操作需要复杂的控制方案且成本高昂，示教学习则因人为干预导致视觉干扰。本项研究提出一种新型主端到机器人（M2R）策略迁移系统，该系统无需机器人即可完成基于力反馈的操作任务教学。人类操作者直接使用控制器演示任务。该控制器在运动学参数上模拟机器人臂，并采用配备力/力矩（F/T）传感器的相同末端执行器来测量力反馈。使用该控制器，操作者无需双边系统即可感知力反馈。所提方法通过基于注视的模仿学习与简易标定方法，可克服主端与机器人之间的领域差异。此外，采用Transformer从F/T传感输入中推断策略。该系统在需要力反馈的瓶盖开启任务中进行了评估。