Stochastic Optimal Control for Robot Manipulation Skill Learning Under Time-Varying Uncertain Environment
提出一种随机最优控制方法，通过高斯过程回归学习时变不确定环境模型，并集成到迭代线性二次高斯控制器中，优化机器人的前馈力、参考轨迹和阻抗参数，仿真和插孔实验验证了有效性。
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.