Training Robots Without Robots: Deep Imitation Learning for Master-to-Robot Policy Transfer
Training Robots Without Robots: Deep Imitation Learning for Master-to-Robot Policy Transfer
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DOI：
10.1109/lra.2023.3262423
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发表时间：
2022-02
影响因子：
5.2
通讯作者：
Heecheol Kim;Y. Ohmura;Akihiko Nagakubo;Y. Kuniyoshi
中科院分区：
文献类型：
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作者：
Heecheol Kim;Y. Ohmura;Akihiko Nagakubo;Y. Kuniyoshi
深度模仿学习在机器人操作方面很有前途，因为它只需要演示样本。在本研究中，深度模仿学习应用于需要力反馈的任务。但现有的论证方法存在不足；双侧遥操作需要复杂的控制方案，而且成本高昂，动觉教学还会受到人为干预造成的视觉干扰。本研究提出一种新的主人对机器人（M2R）策略传递系统，该系统不需要机器人来完成基于教学力反馈的操作任务。人类直接使用控制器演示任务。该控制器类似于机械臂的运动学参数，并使用具有力/扭矩（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，操作员可以在没有双边系统的情况下感受到力反馈。该方法利用基于注视的模仿学习和简单的标定方法克服了主人和机器人之间的域间隙。在此基础上，应用变压器从F/T感官输入中推断策略。提出的系统在一个需要力反馈的开瓶盖任务上进行了评估。
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.