Reinforcement Learning Impedance Control of a Robotic Prosthesis to Coordinate With Human Intact Knee Motion
Reinforcement Learning Impedance Control of a Robotic Prosthesis to Coordinate With Human Intact Knee Motion
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DOI：
10.1109/lra.2022.3179420
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发表时间：
2022-07-01
影响因子：
5.2
通讯作者：
Huang, He
中科院分区：
文献类型：
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作者：
Wu, Ruofan;Li, Minhan;Huang, He
本研究旨在演示强化学习跟踪控制，用于自动配置机器人膝关节假体的阻抗参数。虽然我们之前涉及人类受试者的研究重点是调整阻抗控制参数以满足固定的、主观规定的目标运动曲线，从而实现人在环中连续行走，但在本文中，我们为机器人膝盖开发了一种新的跟踪控制解决方案，以模仿完整膝盖的运动。因此，我们用基于完整膝盖的自动生成的轮廓替换了规定的目标膝盖运动。由于人类适应，完整膝盖的轮廓随着时间的推移而变化，我们在经典控制理论的背景下面临着一个具有挑战性的跟踪控制问题。通过将机器人膝盖的“回声控制”表述为强化学习问题，我们为实时跟踪控制设计提供了一种有前景的新工具，而无需使用数学模型明确表示底层动力学，而这对于人类机器人系统来说很难获得。此外，我们的结果可能会激发未来的研究和新的机器人假肢阻抗控制设计，这些设计可以在完整的肢体和机器人肢体之间进行协调，以实现机器人设备的日常使用。
This study aims to demonstrate reinforcement learning tracking control for automatically configuring the impedance parameters of a robotic knee prosthesis. While our previous studies involving human subjects have focused on tuning the impedance control parameters to meet a fixed, subjectively prescribed target motion profile to enable continuous walking with human-in-the-loop, in this paper we develop a new tracking control solution for a robotic knee to mimic the motion of the intact knee. As such, we replaced the prescribed target knee motion by an automatically generated profile based on the intact knee. As the profile of the intact knee varies over time due to human adaptation, we are presented with a challenging tracking control problem in the context of classical control theory. By formulating the "echo control" of the robotic knee as a reinforcement learning problem, we provide a promising new tool for real-time tracking control design without explicitly representing the underlying dynamics using a mathematical model, which can be difficult to obtain for a human-robot system. Additionally, our results may inspire future studies and new robotic prosthesis impedance control designs that can potentially coordinate between the intact and the robotic limbs toward daily use of the robotic device.