title: Controlling a robot with intention derived from motion.
abstract: We present a novel, sophisticated intention-based control system for a mobile robot built from an extremely inexpensive webcam and radio-controlled toy vehicle. The system visually observes humans participating in various playground games and infers their goals and intentions through analyzing their spatiotemporal activity in relation to itself and each other, and then builds a coherent narrative out of the succession of these intentional states. Starting from zero information about the room, the rules of the games, or even which vehicle it controls, it learns rich relationships between players, their goals and intentions, probing uncertain situations with its own behavior. The robot is able to watch people playing various playground games, learn the roles and rules that apply to specific games, and participate in the play. The narratives it constructs capture essential information about the observed social roles and types of activity. After watching play for a short while, the system is able to participate appropriately in the games. We demonstrate how the system acts appropriately in scenarios such as chasing, follow-the-leader, and variants of tag.
authors.affiliation: Department of Computer Science, Yale University.
authors.forename: Christopher
authors.initials: C
authors.lastname: Crick
authors.forename: Brian
authors.initials: B
authors.lastname: Scassellati
mesh_terms: D005717:Games, Experimental; D006801:Humans; D033182:Intention; D007858:Learning; D009038:Motion; D012371:Robotics / Q000295:instrumentation / Q000379:methods*; D012938:Social Perception
keywords: Artificial intelligence; Interactive behavior; Learning; Robotics; Social cognition