Mindreading for Robots: Predicting Intentions via Dynamical Clustering of Human Postures

Recent advancements in robotics suggest a future where social robots will be deeply integrated in our society. In order to understand humans and engage in finer interactions, robots would greatly benefit from the ability of intention reading: the capacity to discern the high-level goal that is driving the low-level actions of an observed agent. This is particularly useful in joint action scenarios, where human and robot must collaborate to reach a shared goal: if the latter can predict the actions of the former, it will be able to use this information for decision making in order to improve the quality of the cooperation. This research proposes a novel artificial cognitive architecture, based on the developmental robotics paradigm, that can estimate the goals of a human partner engaged in a joint task to modulate synergistic behavior. This is accomplished using unsupervised dynamical clustering of human skeletal data and a hidden semi-Markov chain. The effectiveness of this architecture has been tested through an interactive cooperative experiment involving a block building game, the iCub robot and a human. The results show that the former is able to adopt a collaborative behavior by performing intention reading based on the partner's physical clues.