The Collaborative Mind: Intention Reading and Trust in Human-Robot Interaction

Robots stand at the heart of a techno-scientific revolution which promises to significantly alter the way in which we conceive our society. Recent discoveries point towards a future in which artificial agents will become fully integrated in our social structures, thus becoming important actors in our everyday life. In this scenario, it is of critical importance for these robots to understand us in the most human-like fashion and to be able to assist us in our daily routines. We state that collaboration between humans and robots is fostered by two cognitive skills: intention reading and trust. An agent possessing these abilities would be able to infer the non-verbal intentions of other agents and to evaluate how likely they are to achieve their selected goals. This would allow the robot to jointly understand what kind and which degree of collaboration is required and to provide appropriate assistance. To accomplish this, we propose a novel developmental artificial cognitive architecture that uses unsupervised machine learning and probabilistic models to imbue a humanoid robot with intention reading and trusting capabilities. To test our computational model, we make use of a collaborative block placing game between humans and a robot. Our results show that the synergistic implementation of these cognitive skills enable the robot to cooperate in a meaningful way, with the intention reading model allowing a correct goal prediction and with the trust component enhancing the likelihood of a positive outcome for the task. This demonstrates a new method to enhance human-robot collaborations.