Paper Info
Title
Learning Force Sensory Patterns And Skills From Human Demonstration
Abstract
The motivation behind this work is to transfer force-based assembly skills to robots by using human demonstration, For this purpose, we model the skills as a sequence of contact formations (which describe how a workpiece touches its environment) and desired transitions between contact formations. In this paper, we present a method of identifying single-ended contact formations from force sensor patterns. Instead of using geometric models of the workpieces, fuzzy logic is used to learn and model the patterns in the force signals. Membership functions are generated automatically from training data and then used by the fuzzy classifier. This classification scheme is used to learn desired sequences of contact formations which comprise a force-based skill. Experimental results are presented which use the technique to extract skill information from human demonstration data.
Year
Venue
Keywords
1997
1997 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION - PROCEEDINGS, VOLS 1-4
membership function,fuzzy logic,solid modeling,force sensor,machine vision,computer science,hidden markov models,fuzzy set theory,geometric model
Field
DocType
ISSN
Force sensor,Training set,Computer science,Fuzzy logic,Classification scheme,Fuzzy set,Artificial intelligence,Fuzzy classifier,Sensory system,Robot
Conference
1050-4729
Citations 
PageRank 
References 
2
0.71
3
Authors
2
Name
Order
Citations
PageRank
Marjorie Skubic11045105.36
Richard A. Volz2864259.12