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 author'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.

翻译：机器学习目前在机器人物体操纵中起着重要作用。 此外,为了对各种物体进行操纵以实现稳健性,防止配置和僵硬的干扰,还需要武力控制。作者的小组透露,通过双边控制模拟学习可以获得武力控制的快速和动态物体操纵。然而,这种方法只适用于能够控制托盘的机器人,而不适用于只能遵循位置或速度指令的机器人,如许多商业机器人。在此研究中,提出了对速度控制的机器人进行双边控制模拟学习的方法。提议的方法的有效性通过抽取任务进行实验性核查。