Deep imitation learning is a promising method for dexterous robot manipulation because it only requires demonstration samples for learning manipulation skills. In this paper, deep imitation learning is applied to tasks that require force feedback, such as bottle opening. However, simple visual feedback systems, such as teleoperation, cannot be applied because they do not provide force feedback to operators. Bilateral teleoperation has been used for demonstration with force feedback; however, this requires an expensive and complex bilateral robot system. In this paper, a new master-to-robot (M2R) transfer learning system is presented that does not require robots but can still teach dexterous force feedback-based manipulation tasks to robots. The human directly demonstrates a task using a low-cost controller that resembles the kinematic parameters of the robot arm. Using this controller, the operator can naturally feel the force feedback without any expensive bilateral system. Furthermore, the M2R transfer system can overcome domain gaps between the master and robot using the gaze-based imitation learning framework and a simple calibration method. To demonstrate this, the proposed system was evaluated on a bottle-cap-opening task that requires force feedback only for the master demonstration.

本文提出了一种新的主控器到机器人(M2R)迁移学习系统，通过低成本控制器和基于注视的模仿学习框架及简单的校准方法，使操作者可以自然地感受到力反馈而不需要昂贵的双边系统，进而克服主控器和机器人之间的域差异，使得机器人也能够完成需要力反馈的灵巧操作任务，作者在瓶盖开启任务上进行了验证。

无需机器人的机器人训练:基于深度模仿学习的主控策略传递