Cognitive Robotic Engine: Behavioral Perception Architecture for Human-Robot Interaction

Abstract

For personal or domestic service robots to be successful in market, it is essential for them to have capability of natural and dependable interaction with human. However, such a natural and dependable human-robot interaction (HRI) is not so easy to accomplish, as it involves a high level of robotic intelligence for recognizing and understanding human speech, facial expression, gesture, behavior, and intention as well as for generating a proper response to human with artificial synthesis. It is our view that first key step toward a successful deployment of HRI is to level up dependability of a robot for recognizing intention of human counterpart. For instance, to date, robotic recognition of human speech, as well as human gestures, facial expressions, let alone human intention, is still quite unreliable in a natural setting, despite tremendous effort by researchers to perfect machine perception. We observe that robustness and dependability human enjoys in human-human interaction may not merely come from fact that human has powerful perceptual organs such as eyes and ears but human is capable of executing a series of behaviors associated with a perceptual goal, for instance, behaviors related to collection of additional evidences till decision is sufficiently credible. In analogy, we claim here that dependability of robotic recognition of human intention for HRI may not come from perfection of individual capabilities for recognizing speech, gesture, facial expression, etc. But, it comes with automatic generation of robotic behaviors that makes sure of reaching a credible decision for given perceptual goal.We present here “Cognitive Robotic Engine (CRE)” that automatically generates such perceptual behaviors as selecting and collecting an optimal set of evidences, for dependable and robust recognition of human intention under a high level of uncertainty and ambiguity. CRE is to demonstrate that dependability of robotic perception may not come from the perfection of individual components for perception, but from the integration of individual components into dependable system behaviors, no matter how imperfect and uncertain individual components may be. CRE presents a novel robotic architecture featuring 1) spontaneous establishment of ad-hoc missions in connection to perceptual goals, 2) determination of an optimal set of evidences to be selected and/or collected for processing based on in-situ monitoring of current situation, 3) integration of such behavioral building blocks as mission management, evidence selection, evidence