Learning the Inverse Dynamics of a Robot Arm by Auto-Imitation

被引：0

作者：

Kalveram, Karl Theodor ^{[1
]}

Seyfarth, Andre ^{[2
]}

机构：

[1] Univ Duesseldorf, Inst Expt Psychol, Cybernet Psychol, D-40225 Dusseldorf, Germany

[2] Univ Jena, Inst Sport Sci, Locomot Lab, D-07743 Jena, Germany

来源：

IMCIC 2010: INTERNATIONAL MULTI-CONFERENCE ON COMPLEXITY, INFORMATICS AND CYBERNETICS, VOL I (POST-CONFERENCE EDITION) | 2010年

关键词：

inverse adaptive control; sensorimotor learning; inverse dynamics model; auto-imitation; MOTOR CONTROL; MODEL;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Auto-imitation is a type of unsupervised learning which enables a controller to quickly acquire the inverse dynamics of plants. The basics of the method are demonstrated using a one-segment robot arm. If embedded into a learner-operator schema, the inverse model can be updated while the robot performs purposive or even arbitrary movements. So, the proposed algorithm would be an alternative to supervised learning, for instance via feedback-error. It is presumed that auto imitation like learning governs especially social learning in humans.

引用

页码：230 / 235

页数：6

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