Stochastic Optimal Control for Robot Manipulation Skill Learning Under Time-Varying Uncertain Environment

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 t...

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Bibliographic Details
Published inIEEE transactions on cybernetics Vol. 54; no. 4; pp. 2015 - 2025
Main Authors Liu, Xing, Liu, Zhengxiong, Huang, Panfeng
Format Journal Article
LanguageEnglish
Published United States IEEE 01.04.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Control methods
Environment models
Feedforward control
Gaussian process
Gaussian processes
Impedance
iterative linear quadratic Gaussian with learned external dynamic (ILQG-LED) method
Iterative methods
Manipulator dynamics
model-based reinforcement learning
Nonlinear systems
Optimal control
Parameters
Robot arms
Robot control
robot manipulation skill
robot-environment interaction
Robots
stochastic optimal manipulation control
Stochastic processes
System dynamics
Task complexity
Time-varying systems
time-varying uncertain environment
Trajectory
Unknown environments
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Summary: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, the feedforward force, the reference trajectory, as well as the impedance parameters, subject to time-varying environment dynamics. The comparative simulation studies verify the advantages of the presented method, and the experimental studies of the peg-hole-insertion task prove that this method can deal with complex manipulation tasks.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2022.3211440