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International audience; In human-human interaction, para-verbal and non-verbal communication are naturally aligned and synchronized. The difficulty encountered during the coordination between speech and head gestures concerns the conveyed meaning, the way of performing the gesture with respect to speech characteristics , their relative temporal arrangement, and their coordinated organization in a phrasal structure of utterance. In this research, we focus on the mechanism of mapping head gestures and speech prosodic characteristics in a natural human-robot interaction. Prosody patterns and head gestures are aligned separately as a parallel multi-stream HMM model. The mapping between speech and head gestures is based on Coupled Hidden Markov Models (CHMMs), which could be seen as a collection of HMMs, one for the video stream and one for the audio stream. Experimental results with Nao robot are reported.

In this paper, we present an online adaptation approach and a long-term learning approach for socially assistive robotic (SAR) systems that aim to provide customized help protocols through motivation, encouragements, and companionship to users suffering from physical and/or cognitive changes related to stroke, aging and Alzheimer’s disease.

This paper describes a hands-off therapist robot that monitors, assists, encourages, and socially interacts with post-stroke users engaged in rehabilitation exercises. We investigate the role of the robot’s personality in the hands-off therapy process, focusing on the relationship between the level of extroversion-introversion of the robot and the user. The experiments map the extroversion-introversion personality dimension and the spectrum of therapy styles that range from challenging to nurturing. The experimental results provide first evidence for the preference of personality matching in the assistive domain.

Navigation in unknown or partially unknown environments remains one of the biggest challenges in today\'s mobile robotics. Environmental modeling, perception, localization and mapping are all needed for a successful approach. The contribution of this paper resides in the extension of the fingerprint concept (circular list of features around the robot) with uncertainty modeling, in order to improve localization and allow for automatic map building. The uncertainty is defined as the probability of a feature of being present in the environment when the robot perceives it. The whole approach is presented in details and viewed in a topological optic. Experimental results of the perception and localization capabilities with a mobile robot equipped with two 180° laser range finders and an omni-directional camera are reported.