Computational models of classical conditioning: a comparative study
Proceedings of the fifth international conference on simulation of adaptive behavior on From animals to animats 5
A Bayesian Approach to Temporal Data Clustering using Hidden Markov Models
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Sheep and wolves: test bed for human-robot interaction
CHI '06 Extended Abstracts on Human Factors in Computing Systems
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In this paper, we present a human-robot teaching framework that uses "virtual" games as a means for adapting a robot to its user through natural interaction in a controlled environment. We present an experimental study in which participants instruct an AIBO pet robot while playing different games together on a computer generated playfield. By playing the games in cooperation with its user, the robot learns to understand the user's natural way of giving multimodal positive and negative feedback. The games are designed in a way that the robot can reliably anticipate positive or negative feedback based on the game state and freely explore its user's reward behavior by making good or bad moves. We implemented a two-staged learning method combining Hidden Markov Models and a mathematical model of classical conditioning to learn how to discriminate between positive and negative feedback. After finishing the training the system was able to recognize positive and negative reward based on speech and touch with an average accuracy of 90.33%.