Understanding robot motor capability using information-theory-based approach

Authors:
Hsien-I Lin;C. S. George Lee
Affiliations:
School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN;School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN
Venue:
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Year:
2009

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Abstract

Robot skills are usually learned from the so-called learning-from-human-demonstration methods. However, with the limitation of robot motor capability, a robot may not be able to duplicate human motor skills with the same motor performance. To alleviate the problem, one of the possible solutions is to know robot motor capability in advance. Thus, we develop a quantitative measure of a robot motor system, called a pseudo index of motor performance (pIp), and utilize it to compare with the index of performance (Ip) of a human motor system. To investigate the Ip, we propose an information-theory- based method to characterize a robot motor system. Computer simulations and experiments with a PUMA 560 robot will be conducted to validate the proposed information-theory- based method.