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 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