We address the problem of transferring human control strategies (HCS) from an expert model to an apprentice model. The proposed algorithm allows us to develop useful apprentice models that incorporate some of the robust aspects of the expert HCS models. We first describe our experimental platform, a real-time graphic driving simulator, for collecting and modeling human control strategies. Then, we discuss an adaptive neural network learning architecture for abstracting HCS models. Next, we define a hidden Markov model (HMM) based similarity measure which allows us to compare different human control strategies. This similarity measure is combined subsequently with simultaneously perturbed stochastic approximation to develop our proposed transfer learning algorithm. In this algorithm, an expert HCS model influences both the structure and the parametric representation of the eventual apprentice HCS model. Finally, we describe some experimental results of the proposed algorithm.
Transfer of human control strategy based on similarity measure
IEEE International Conference on Robotics and Automation, 1999 ; 4 ; 3134-3139
1999
6 Seiten, 11 Quellen
Aufsatz (Konferenz)
Englisch
Improved similarity measure-based graph embedding for face recognition
British Library Online Contents | 2012
|A skeletal measure of 2D shape similarity
British Library Online Contents | 2004
|Measure of similarity between images based on the codispersion coefficient
British Library Online Contents | 2012
|An attention based similarity measure used to identify image clusters
IET Digital Library Archive | 2005
|The Measure of Motion Similarity for Robotics Application
Springer Verlag | 2022
|