Robot skill transfer based on B-spline fuzzy controllers for force-control tasks

Human-beings can easily describe their behaviour by IF-THEN rules, which can be transferred from one task to another with slight local changes. However, standard techniques for function approximation like neural networks or associative memories are unable to work with rules. We introduce a method fo...

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Bibliographic Details
Published inProceedings - IEEE International Conference on Robotics and Automation Vol. 2; pp. 1170 - 1175 vol.2
Main Authors Ferch, M., Zhang, J., Knoll, A.
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 1999
Subjects
Artificial intelligence
Data mining
Force control
Fuzzy control
Humans
Intelligent networks
Optimal control
Robot kinematics
Robot sensing systems
Spline
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Summary:Human-beings can easily describe their behaviour by IF-THEN rules, which can be transferred from one task to another with slight local changes. However, standard techniques for function approximation like neural networks or associative memories are unable to work with rules. We introduce a method for extracting and importing human readable rules from and to a B-spline fuzzy controller. Rule import is used to initialise a B-spline fuzzy controller with a priori knowledge to decrease the learning time and overcome the problem of partially trained B-spline controllers. In the experimental section we show how a set of rules for a two arm cooperation task are generated through "learning-by-doing" and transferred to a robot screwing operation. The successful experiment shows how rule-based knowledge can be used for skill transfer in similar tasks.
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ISBN:9780780351806
0780351800
ISSN:1050-4729
2577-087X
DOI:10.1109/ROBOT.1999.772520