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Robot Learning Theory of Mind through Self-Observation: Exploiting the Intentions-Beliefs Synergy
Assuming that learning of beliefs can take place by observing own decision and beliefs estimation processes in partially observable environments and using a simple feed-forward deep learning model, we show that when learning to predict others' intentions and actions, faster and more accurate predictions can be acquired if beliefs attribution is learnt simultaneously with action and intentions prediction.
Transferring Adaptive Theory of Mind to social robots: insights from developmental psychology to robotics
Despite the recent advancement in the social robotic field, important limitations restrain its progress and delay the application of robots in everyday scenarios.
Functional advantages of an adaptive Theory of Mind for robotics: a review of current architectures
The aim of the present paper was to determine in which way and how often ToM features are integrated in the architectures analyzed, and if they provide robots with the associated functional advantages.
