Bilateral Control-Based Imitation Learning for Velocity-Controlled Robot
Bilateral Control-Based Imitation Learning for Velocity-Controlled Robot
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DOI：
10.1109/isie45552.2021.9576326
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发表时间：
2021-03
期刊：
影响因子：
--
通讯作者：
S. Sakaino
中科院分区：
文献类型：
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作者：
S. Sakaino
机器学习在机器人对象操作中发挥着重要作用。此外，力控制是必要的操纵各种对象，以实现对配置和刚度的扰动的鲁棒性。作者的研究小组发现，快速和动态的物体操作与力控制，可以通过双边控制为基础的模仿学习。然而，该方法仅适用于可以控制扭矩的机器人，而不适用于像许多市售机器人那样只能遵循位置或速度命令的机器人。然后，在本研究中，提出了一种方法来实现双边控制的模仿学习速度控制机器人。通过一个拖地任务的实验验证了该方法的有效性。
Machine learning is now playing important role in robotic object manipulation. In addition, force control is necessary for manipulating various objects to achieve robustness against perturbations of configurations and stiffness. The au-thor's group revealed that fast and dynamic object manipulation with force control can be obtained by bilateral control-based imitation learning. However, the method is applicable only in robots that can control torque, while it is not applicable in robots that can only follow position or velocity commands like many commercially available robots. Then, in this research, a way to implement bilateral control-based imitation learning to velocity-controlled robots is proposed. The validity of the proposed method is experimentally verified by a mopping task.