An experimental evaluation of reinforcement learning for gain scheduling

Authors:
Ivan S. K. Lee;Henry Y. K. Lau;Lionel C. C. Wai
Affiliations:
Department of Industrial & Manufacturing Systems Engineering, The University of Hong Kong, Pokfulam, Hong Kong, PRC;Department of Industrial & Manufacturing Systems Engineering, The University of Hong Kong, Pokfulam, Hong Kong, PRC;Department of Industrial & Manufacturing Systems Engineering, The University of Hong Kong, Pokfulam, Hong Kong, PRC
Venue:
Design and application of hybrid intelligent systems
Year:
2003

Citing 8
Cited 0

Historical perspective and state of the art in robot force control

International Journal of Robotics Research
Whole arm manipulation

Proceedings of the 4th international symposium on Robotics Research
Technical Note: \cal Q-Learning

Machine Learning
Learning to act using real-time dynamic programming

Artificial Intelligence - Special volume on computational research on interaction and agency, part 1
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
Learning to Predict by the Methods of Temporal Differences

Machine Learning
Dynamic Programming

Dynamic Programming
An overview of robot force control

Robotica

Quantified Score

Hi-index	0.00

Visualization

Abstract

Most conventional approaches of force control for surface following operations require fine tuning of the feedback gain to be successful. The optimal feedback gain values of the force control loop are either analytically derived based on the geometrical model of the surface or determined empirically. This paper presents an experimental investigation of using reinforcement learning techniques to generate a gain schedule for an unknown surface. The result is compared with fixed and constant gain values.