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Robotics technology has made progress on a number of important issues in the last decade. However many challenges remain when it comes to the development of systems for human-robot interaction. This paper presents a case study featuring a robust dialogue interface for humanrobot communication onboard an intelligent wheelchair. Underlying this interface is a sophisticated software architecture which allows the chair to perform real-time, robust tracking of the dialogue state, as well as select appropriate responses using rich probabilistic representations. The paper also examines the question of rigorous validation of complex human-robot interfaces by evaluating the proposed interface in the context of a standardized rehabilitation task domain.

Abstract-Partially Observable Markov Decision Processes (POMDPs) offer a powerful mathematical framework for making optimal action choices in noisy and/or uncertain environments, in particular, allowing us to merge localization and decision-making for mobile robots. While advancements in POMDP techniques have allowed the use of much larger models, POMDPs for robot navigation are still limited by large state space requirements for even small maps. In this work, we propose a method to automatically generate a POMDP representation of an environment. By using variable resolution decomposition techniques, we can take advantage of characteristics of the environment to minimize the number of states required, while maintaining the level of detail required to find a robust and efficient policy. This is accomplished by automatically adjusting the level of detail required for planning at a given region, with few states representing large open areas, and many smaller states near objects. We validate this algorithm in POMDP simulations, a robot simulator as well as an autonomous robot.

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