Human-robot collaborative learning system for inspection

Kartoun Uri, Stern Helman, Yael Edan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

This paper presents a collaborative reinforcement learning algorithm, CQ(λ), designed to accelerate learning by integrating a human operator into the learning process. The CQ(λ) -learning algorithm enables collaboration of knowledge between the robot and a human; the human, responsible for remotely monitoring the robot, suggests solutions when intervention is required. Based on its learning performance, the robot switches between fully autonomous operation, and the integration of human commands. The CQ(λ) -learning algorithm was tested on a Motoman UP-6 fixed-arm robot required to empty the contents of a suspicious bag. Experimental results of comparing the CQ(λ) with the standard Q(λ), indicated the superiority of the CQ(λ) while achieving an improvement of 21.25% in the average reward.

Original languageEnglish GB
Title of host publication2006 IEEE International Conference on Systems, Man and Cybernetics
PublisherInstitute of Electrical and Electronics Engineers
Pages4249-4255
Number of pages7
ISBN (Print)1424401003, 9781424401000
DOIs
StatePublished - 1 Jan 2006
Event2006 IEEE International Conference on Systems, Man and Cybernetics - Taipei, Taiwan, Province of China
Duration: 8 Oct 200611 Oct 2006

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume5
ISSN (Print)1062-922X

Conference

Conference2006 IEEE International Conference on Systems, Man and Cybernetics
Country/TerritoryTaiwan, Province of China
CityTaipei
Period8/10/0611/10/06

ASJC Scopus subject areas

  • General Engineering

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