Perception is Only Real When Shared: A Mathematical Model for Collaborative Shared Perception in Human-Robot Interaction - Matarese, Marco; Rea, Francesco; Sciutti, Alessandra (Frontiers in Robotics and AI, 2022)
In everyday collaborative tasks, partners have to build a shared understanding of their environment by aligning their perceptions and establishing a common ground. This is one of the aims of shared perception: revealing characteristics of the individual perception to others, with whom we are sharing the same environment. In this regard, social cognitive processes, such as joint attention and perspective-taking, participate in forming a shared perception. From a Human-Robot Interaction (HRI) perspective, robots would benefit from the ability to establish shared perception with humans and a common understanding of the environment with their partners. In this work, we wanted to assess whether a robot, considering the differences in perception between itself and its partner, could be more effective in its helping role and to what extent this improves task completion and the interaction experience. For this purpose, we designed a mathematical model for collaborative shared perception that aims to maximise the collaborators' knowledge of the environment when there are asymmetries in perception. Moreover, we instantiated and tested our model via a real HRI scenario. The experiment consisted of a cooperative game in which participants had to build towers of Lego bricks, while the robot took the role of a suggester. In particular, we conducted experiments using two different robot's behaviours. In one condition, based on shared perception, the robot gave suggestions by taking into account the partners' point of view and by using its inference about their common ground to select the most informative hint. In the other condition, it simply indicated the brick that would have yielded a higher score from its individual perspective. The adoption of shared perception in the selection of suggestions led to better performances in all the instances of the game where the visual information was not a priori common to both agents. However, the subjective evaluation of the robot's behaviour did not change between conditions.