Multimodal Interaction Management for Tour-Guide Robots Using Bayesian Networks

Abstract

Abstract − In this paper, we propose a Bayesian network framework for managing interactivity between a tour-guide robot and visitors in mass exhibition conditions, through robust interpretation of multi-modal signals. We report on methods and experiments interpreting speech and laser scanner signals in the spoken dialogue management system of the autonomous tour-guide robot RoboX, successfully deployed at the Swiss National Exhibition (Expo.02). A correct interpretation of a user’s (visitor’s) goal or intention at each dialogue state is a key issue for successful speech-based interaction in voice-enabled communication between robots and visitors. We introduce a Bayesian network approach for combining noisy speech recognition results with noise-independent data from a laser scanner, in order to infer the visitors ’ goal under the uncertainty intrinsic to these two modalities. We demonstrate the effectiveness of the approach by simulation based on real observations during experiments with the tour-guide robot RoboX at Expo.02. I.