Mightability maps: A perceptual level decisional framework for co-operative and competitive human-robot interaction

Interestingly Humans are able to maintain rough estimations of visibility, reachability and other capabilities of not only themselves but of the person they are interacting with. Studies in neuroscience and psychology suggest that from the age of 12-15 months children start to understand the occlusion of others line-of-sight and from the age of 3 years they start to develop the ability, termed as perceived reachability for self and for others. As such capabilities evolve in the children, they start showing intuitive and proactive behavior by perceiving various abilities of the human partner. Inspired from such studies, which suggest that visuo-spatial perception plays an important role in Human-Human interaction, we propose to equip our robot with the capabilities to maintain various types of reachabilities and visibilities information of itself and of the human partner in the shared workspace. Since these analyses will be basically perceived by performing a virtual action onto the agent and roughly estimating what that agent might be able to ´see´ and ´reach´ in 3D space, we term these representations as Mightability Maps. By applying various set operations on Weighted Mightability Maps, robot could perceive a set of candidate solutions in real time for various tasks. We show its application in exhibiting two different behaviors of robot: co-operative and competitive. These maps are also quick to compute and could help in developing higher-level decisional capabilities in the robot.