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.
If we are to build human-like robots that can interact naturally with people, our robots must know not only about the properties of objects but also the properties of animate agents in the world. One of the fundamental social skills for humans is the attribution of beliefs, goals, and desires to other people. This set of skills has often been called a “theory of mind.” This paper presents the theories of Leslie (1994) and Baron-Cohen (1995) on the development of theory of mind in human children and discusses the potential application of both of these theories to building robots with similar capabilities. Initial implementation details and basic skills (such as finding faces and eyes and distinguishing animate from inanimate stimuli) are introduced. I further speculate on the usefulness of a robotic implementation in evaluating and comparing these two models. Abstract This paper addresses an important issue in learning from demonstrations that are provided by “naive” human teachers—people who do not have expertise in the machine learning algorithms used by the robot. We therefore entertain the possibility that, whereas the average human user may provide sensible demonstrations from a human’s perspective, these same demonstrations may be insufficient, incomplete, ambiguous, or otherwise “flawed” from the perspective of the training set needed by the learning algorithm to generalize properly. To address this issue, we present a system where the robot is modeled as a socially engaged and socially cognitive learner. We illustrate the merits of this approach through an example where the robot is able to correctly learn from “flawed” demonstrations by taking the visual perspective of the human instructor to clarify potential ambiguities.  We propose that an important aspect of human-robot interaction is perspective-taking. We show how perspective-taking occurs in a naturalistic environment (astronauts working on a collaborative project) and present a cognitive architecture for performing perspective-taking called Polyscheme. Finally, we show a fully integrated system that instantiates our theoretical framework within a working robot system. Our system successfully solves a series of perspective-taking problems and uses the same frames of references that astronauts do to facilitate collaborative problem solving with a person.  Humans constantly generate and solve ambiguities while interacting with each other in their every day activities. Hence, having a robot that is able to solve ambiguous situations is essential if we aim at achieving a fluent and acceptable human-robot interaction. We propose a strategy that combines three mechanisms to clarify ambiguous situations generated by the human partner. We implemented our approach and successfully performed validation tests in several different situations both, in simulation and with the HRP-2 robot.