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Seminar talk

Planning for Social Interaction in Embodied Robot Systems, R. Petrick, slides from a talk presented at the Computational Linguistics Colloquium, Universität Potsdam, Germany, 2011-11-21.

[ slides ]

Abstract

In recent years, developers of robot systems have begun to consider the social aspects of human-robot interaction: robots coexisting with humans must not only be able to carry out physical tasks in the world, but also be able to interact with humans in a socially appropriate manner. However, achieving this goal requires endowing a robot with the ability to recognise, understand, and generate a range of multimodal social signals (e.g., gesture, facial expression, language, etc.), in order to interpret and respond to humans in a realistic way.

JAMES (Joint Action for Multimodal Embodied Social Systems) is a new European Commission-funded project (coordinated by the University of Edinburgh), exploring the problem of social interaction in human-robot environments. JAMES aims to develop a socially intelligent robot that combines task-based behaviour with the ability to understand and respond to a wide range of embodied, multimodal, communicative signals in a socially appropriate manner. In particular, JAMES takes a multidisciplinary approach to this problem, combining computer vision, natural language processing, machine learning, automated planning, and human-human studies on social interaction, to develop an embodied robot system that supports realistic, multi-party interactions in a bartending scenario.

In this talk, I will present an overview of the JAMES project and highlight its main research themes, with emphasis on the role of automated planning and reasoning in the project. In particular, I will discuss how general purpose knowledge-level planning techniques are used to generate high-level plans that mix physical robot operations with certain types of social behaviour (e.g., speech act-based human-robot interaction), by viewing the plan generation task as an instance of the problem of planning with incomplete information and sensing actions.