NDLI: Assessing internal models for faster learning of robotic assembly

Content Provider	IEEE Xplore Digital Library
Author	Marvel, J.A. Newman, W.S.
Copyright Year	2010
Description	Author affiliation: Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH 44106, USA (Marvel, J.A.; Newman, W.S.)
Abstract	This work investigates what makes a robotic assembly process “learnable” for the explicit purpose of improving the performance of that process. It has been observed that even stochastic search methods like Genetic Algorithms (GA) can benefit from advanced models of the assembly task. Models built from the results of random samplings of a parameter space have been used previously to predict the performances of parameter sequences not yet evaluated, but the question of what properties of the models actually benefit the optimization remained. A quantitative analysis algorithm is derived and tested on physical assemblies for validation. Results are provided that illustrate the efficacy of the analysis algorithm for prediction-based performance enhancement when such models are used.
Starting Page	2143
Ending Page	2148
File Size	359349
Page Count	6
File Format	PDF
ISBN	9781424450381
ISSN	10504729
DOI	10.1109/ROBOT.2010.5509174
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robotic assembly Predictive models Algorithm design and analysis Stochastic processes Search methods Genetic algorithms Sampling methods Performance evaluation Testing Performance analysis robotic assembly Model building parameter optimization
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software

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