Robots are required to autonomously respond to changing situations. Imitation learning is a promising candidate for achieving generalization performance, and extensive results have been demonstrated in object manipulation. However, cooperative work between humans and robots is still a challenging issue because robots must control dynamic interactions among themselves, humans, and objects. Furthermore, it is difficult to follow subtle perturbations that may occur among coworkers. In this study, we find that cooperative work can be accomplished by imitation learning using bilateral control. Thanks to bilateral control, which can extract response values and command values independently, human skills to control dynamic interactions can be extracted. Then, the task of serving food is considered. The experimental results clearly demonstrate the importance of force control, and the dynamic interactions can be controlled by the inferred action force.

翻译：机器人需要自主地应对不断变化的情况。 模拟学习是实现普及性表现的有希望的候选条件,在物体操纵中也展示了广泛的结果。 然而,人类和机器人之间的合作工作仍然是一个具有挑战性的问题,因为机器人必须控制自己、人类和物体之间的动态互动。此外,很难跟踪同事之间可能出现的微妙干扰。在本研究中,我们发现合作工作可以通过利用双边控制进行模拟学习来完成。由于双边控制,可以独立提取反应价值和命令价值,因此可以提取人类控制动态互动的技能。然后,考虑服务食物的任务。实验结果清楚地表明了武力控制的重要性,动态互动可以由推断的动作力量来控制。