Learning to identify users and predict their destination in a robotic guidance application

Abstract : User guidance systems are relevant to various applications of the service robotics field, among which: smart GPS navigator, robotic guides for museum or shopping malls or robotic wheel chairs for disabled persons. Such a system aims at helping its user to reach its destination in a fairly complex environment. If we assume the system is used in a fixed environment by multiple users for multiple navigation task over the course of days or weeks, then it is possible to take advantage of the user routine: from the initial navigational choice, users can be identified and their goal can be predicted. As a result of these prediction, the guidance system can bring its user to its destination while requiring less interaction. This property is particularly relevant for assisting disabled person for whom interaction is a long and complex task. In this paper, we implement a user guidance system using a dynamic Bayesian model and a topological representation of the environment. This model is evaluated with respect to the quality of its action prediction in a scenario involving 4 human users, and it is shown that in addition to the user identity, the goals and actions of the user are accurately predicted.
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Xavier Perrin, Francis Colas, Cedric Pradalier, Roland Siegwart. Learning to identify users and predict their destination in a robotic guidance application. Field and Service Robotics, Jun 2009, Cambridge, United States. ⟨hal-00746542⟩

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