In order to successfully cooperate with humans, robots need to learn new skills and behaviours from them, for example, through gesture and speech. This type of social learning, achieved in a social context, may be facilitated by humans acting as teachers, in an implicit or explicit manner. The challenge here is to develop new statistical learning methods for social learning that find an optimal level of human intervention in the robot learning process. By adopting a breadth-first, holistic approach that integrates interdisciplinary research on social robotics and machine learning grounded in principles from the social sciences, the objective of this research is to develop computational social abilities that allow robots to behave in a socially intelligent way.
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