In social robotics, robots needs to be able to be understood by humans. Especially in collaborative tasks where they have to share mutual knowledge. For instance, in an educative scenario, learners share their knowledge and they must adapt their behaviour in order to make sure they are understood by others. Learners display behaviours in order to show their understanding and teachers adapt in order to make sure that the learners' knowledge is the required one. This ability requires a model of their own mental states perceived by others: In this paper, we discuss the importance of a cognitive architecture enabling second-order Mutual Modelling for Human-Robot Interaction in educative contexts.
Breazeal @cite_5 and Warnier @cite_19 reproduced the Sally and Anne's test of Wimmer @cite_8 with robots able to perform visual perspective taking. The robot was able to infer the knowledge of a human given the history of his visual experience.
@cite_19 @cite_5 @cite_8
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We have designed and implemented new spatio-temporal reasoning skills for a cognitive robot, which explicitly reasons about human beliefs on object positions. It enables the robot to build symbolic models reflecting each agent's perspective on the world. Using these models, the robot has a better understanding of what humans say and do, and is able to reason on what human should know to achieve a given goal. These new capabilities are also demonstrated experimentally. Future applications for personal robots motivate research into developing robots that are intelligent in their interactions with people. Toward this goal, in this paper we present an integrated socio-cognitive architecture to endow an anthropomorphic robot with the ability to infer mental states such as beliefs, intents, and desires from the observable behavior of its human partner. The design of our architecture is informed by recent findings from neuroscience and embodies cognition that reveals how living systems leverage their physical and cognitive embodiment through simulation-theoretic mechanisms to infer the mental states of others. We assess the robot's mindreading skills on a suite of benchmark tasks where the robot interacts with a human partner in a cooperative scenario and a learning scenario. In addition, we have conducted human subjects experiments using the same task scenarios to assess human performance on these tasks and to compare the robot's performance with that of people. In the process, our human subject studies also reveal some interesting insights into human behavior. ‘A travelling salesman found himself spending the night at home with his wife when one of his trips was unexpectedly cancelled. The two of them were sound asleep, when in the middle of the night there was a loud knock at the front door. The wife woke up with a start and cried out, ‘Oh, my God! It’s my husband!* Whereupon the husband leapt out from the bed, ran across the room and jumped out the window.’ Schank and Abelson, 1977, p. 59.