TY - CONF
T1 - Towards socially adaptive robots
T2 - 8th IEEE-RAS Int Conf on Humanoid Robots
AU - Francois, D.
AU - Polani, D.
AU - Dautenhahn, K.
N1 - “This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.” DOI: 10.1109/ICHR.2008.4756004
PY - 2009
Y1 - 2009
N2 - Automatically detecting different styles of play in human-robot interaction is a key challenge towards adaptive robots, i.e. robots that are able to regulate the interactions and adapt to different interaction styles of the robot users. In this paper we present a novel algorithm for pattern recognition in human-robot interaction, the Cascaded Information Bottleneck Method. We apply it to real-time autonomous recognition of human-robot interaction styles. This method uses an information theoretic approach and enables to progressively extract relevant information from time series. It relies on a cascade of bottlenecks, the bottlenecks being trained one after the other according to the existing Agglomerative Information Bottleneck Algorithm. We show that a structure for the bottleneck states along the cascade emerges and we introduce a measure to extrapolate unseen data. We apply this method to real-time recognition of Human-Robot Interaction Styles by a robot in a detailed case study. The algorithm has been implemented for real interactions between humans and a real robot. We demonstrate that the algorithm, which is designed to operate real time, is capable of classifying interaction styles, with a good accuracy and a very acceptable delay. Our future work will evaluate this method in scenarios on robot-assisted therapy for children with autism.
AB - Automatically detecting different styles of play in human-robot interaction is a key challenge towards adaptive robots, i.e. robots that are able to regulate the interactions and adapt to different interaction styles of the robot users. In this paper we present a novel algorithm for pattern recognition in human-robot interaction, the Cascaded Information Bottleneck Method. We apply it to real-time autonomous recognition of human-robot interaction styles. This method uses an information theoretic approach and enables to progressively extract relevant information from time series. It relies on a cascade of bottlenecks, the bottlenecks being trained one after the other according to the existing Agglomerative Information Bottleneck Algorithm. We show that a structure for the bottleneck states along the cascade emerges and we introduce a measure to extrapolate unseen data. We apply this method to real-time recognition of Human-Robot Interaction Styles by a robot in a detailed case study. The algorithm has been implemented for real interactions between humans and a real robot. We demonstrate that the algorithm, which is designed to operate real time, is capable of classifying interaction styles, with a good accuracy and a very acceptable delay. Our future work will evaluate this method in scenarios on robot-assisted therapy for children with autism.
U2 - 10.1109/ICHR.2008.4756004
DO - 10.1109/ICHR.2008.4756004
M3 - Paper
SP - 353
EP - 359
Y2 - 1 December 2008 through 3 December 2008
ER -