Stochastic Optimal Control for Robot Manipulation Skill Learning Under Time-Varying Uncertain Environment.
作者：
Liu, Xing;Liu, Zhengxiong;Huang, Panfeng
作者机构：
Research Center for Intelligent Robotics, School of Astronautics, and the National Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University, Xi’an, China
语种：
英文
期刊：
IEEE Transactions on Cybernetics
ISSN：
2168-2267
年：
2024
卷：
PP
页码：
2015-2025
基金类别：
10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61725303 and 62103334);10.13039/501100002858-China Postdoctoral Science Foundation (Grant Number: 2021M702669)
摘要：
In this article, a novel stochastic optimal control method is developed for robot manipulator interacting with a time-varying uncertain environment. The unknown environment model is described as a nonlinear system with time-varying parameters as well as stochastic information, which is learned via the Gaussian process regression (GPR) method as the external dynamics. Integrating the learned external dynamics as well as the stochastic uncertainties, the complete interaction system dynamics are obtained. Then the iterative linear quadratic Gaussian with learned external dynamics (ILQG-LEDs) method is presented to obtain the optimal manipulation control parameters, namely, t...
