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
Integrated learning for interactive synthetic characters
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Development of an Autonomous Quadruped Robot for Robot Entertainment
Autonomous Robots - Special issue on autonomous agents
A Bayesian Approach to Temporal Data Clustering using Hidden Markov Models
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Mobile Robot Miniaturisation: A Tool for Investigation in Control Algorithms
The 3rd International Symposium on Experimental Robotics III
Sheep and wolves: test bed for human-robot interaction
CHI '06 Extended Abstracts on Human Factors in Computing Systems
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
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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 and receiving instruction and feedback from its user, the robot learns to understand the user's typical way of giving multimodal positive and negative feedback. The games are designed in such a way that the robot can reliably predict positive or negative feedback based on the game state and 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. The system combines multimodal speech and touch input for reliable recognition. After finishing the training, the system was able to recognize positive and negative reward with an average accuracy of 90.33%.