Li Yanan, Zhou Xiaodong, Zhong Junpei, Li Xuefang
Department of Engineering and Design, University of Sussex, Brighton, United Kingdom.
Beijing Institute of Control Engineering, Beijing, China.
Front Robot AI. 2019 Aug 27;6:78. doi: 10.3389/frobt.2019.00078. eCollection 2019.
Impedance control has been widely used in robotic applications where a robot has physical interaction with its environment. However, how the impedance parameters are adapted according to the context of a task is still an open problem. In this paper, we focus on a physical training scenario, where the robot needs to adjust its impedance parameters according to the human user's performance so as to promote their learning. This is a challenging problem as humans' dynamic behaviors are difficult to model and subject to uncertainties. Considering that physical training usually involves a repetitive process, we develop impedance learning in physical training by using iterative learning control (ILC). Since the condition of the same iteration length in traditional ILC cannot be met due to human variance, we adopt a novel ILC to deal with varying iteration lengthes. By theoretical analysis and simulations, we show that the proposed method can effectively learn the robot's impedance in the application of robot-assisted physical training.
阻抗控制已广泛应用于机器人与环境进行物理交互的机器人应用中。然而，阻抗参数如何根据任务上下文进行调整仍然是一个未解决的问题。在本文中，我们专注于一种物理训练场景，其中机器人需要根据人类用户的表现调整其阻抗参数，以促进他们的学习。这是一个具有挑战性的问题，因为人类的动态行为难以建模且存在不确定性。考虑到物理训练通常涉及一个重复过程，我们通过使用迭代学习控制（ILC）来开发物理训练中的阻抗学习。由于人类差异，传统ILC中相同迭代长度的条件无法满足，我们采用一种新颖的ILC来处理变化的迭代长度。通过理论分析和仿真，我们表明所提出的方法可以在机器人辅助物理训练的应用中有效地学习机器人的阻抗。
