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.

Learning user habits for semi-autonomous navigation using low throughput interfaces

“…Previous work using this approach for navigation of a real robot yielded encouraging results using speech or emulated BCI user interfaces [4], [3]. We plan to further test the proposed approach using similar setups.…”

“…We have previously shown how this approach can be used to navigate in both unknown [3] and known environments [4]. In this paper we further test the proposed framework for learning user habits when navigating in known environments.…”