Target-Directed Locomotion of a Snake-Like Robot Based on Path Integral Reinforcement Learning

被引：0

作者：

Fang Y. ^{[1
]}

Zhu W. ^{[1
]}

Guo X. ^{[1
]}

机构：

[1] Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin

来源：

Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2019年 / 32卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Path Integral; Reinforcement Learning; Snake-Like Robot; Stochastic Optimal Control; Target-Directed;

D O I：

10.16451/j.cnki.issn1003-6059.201901001

中图分类号：

学科分类号：

摘要：

Path integral is derived from stochastic optimal control. It is a numerical iteration method and solves the problem of the optimal control about continuous nonlinear systems at a high convergence speed without system model. A policy improvement algorithm based on path integral reinforcement learning is proposed for the target-directed locomotion of a snake-like robot in this paper. The path integral reinforcement learning approach is employed to learn the parameters of the snake-like robot serpentine equation, and the robot is controlled to arrive at the target position fast without contacting obstacles in simulation environment. Moreover, the robot with the priori knowledge from the simulation in real environment can complete the task well. Experimental result verifies the validity of the propose algorithm. © 2019, Science Press. All right reserved.

引用

页码：1 / 9

页数：8

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