Abstract
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%.
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Austermann, A., Yamada, S. (2008). Teaching a Pet Robot through Virtual Games. In: Prendinger, H., Lester, J., Ishizuka, M. (eds) Intelligent Virtual Agents. IVA 2008. Lecture Notes in Computer Science(), vol 5208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85483-8_32
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DOI: https://doi.org/10.1007/978-3-540-85483-8_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85482-1
Online ISBN: 978-3-540-85483-8
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