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In this paper, we propose a method to recognize human body movements and we combine it with the contextual knowledge of human-robot collaboration scenarios provided by an object affordances framework that associates actions with its effects and the objects involved in them. The aim is to equip humanoid robots with action prediction capabilities, allowing them to anticipate effects as soon as a human partner starts performing a physical action, thus enabling interactions between man and robot to be fast and natural. We consider simple actions that characterize a human-robot collaboration scenario with objects being manipulated on a table: inspired from automatic speech recognition techniques, we train a statistical gesture model in order to recognize those physical gestures in real time. Analogies and differences between the two domains are discussed, highlighting the requirements of an automatic gesture recognizer for robots in order to perform robustly and in real time. |
