Miguel Faria

TL;DR: The hardware and software infrastructure that supports such rich form of interaction in ASD therapy while featuring a fully autonomous robot is described, as well as the design methodology that guided the development of the INSIDE system.

TL;DR: This paper proposes an approach based on Collaborative Probabilistic Movement Primitives to generate the robot's movements, exploiting predictability and legibility of movement to express intentions through motion.

TL;DR: Sphero and BB-8, two robots with a simple spherical body devoid of verbal and other complex communication methods, are used to investigate how they can communicate intention to people and it is concluded that the use of these behaviors allows a robot to effectively communicate intention as well as create a bond with the participant.

TL;DR: In this article, the authors explore implicit communication between humans and robots through movement in multi-party (or multi-user) interactions, by considering that legibility depends on all human users involved in the interaction and should take into consideration how each of them perceives the robot's movements from their respective points-of-view.

TL;DR: This paper contributes a reinforcement learning-based approach that allows the robot to reason about its own ability to successfully complete the task given the current target pose and indirectly adjust that pose by prompting the human user.