For personal or domestic service robots to be successful in the market, it is essential for them to have the 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 the first key step toward a successful deployment of HRI is to level up the dependability of a robot for recognizing the intention of the 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 the tremendous effort by researchers to perfect the machine perception. We observe that the robustness and dependability human enjoys in human-human interaction may not merely come from the 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, the behaviors related to the collection of additional evidences till the decision is sufficiently credible. In analogy, we claim here that the dependability of robotic recognition of human intention for HRI may not come from the perfection of the individual capabilities for recognizing speech, gesture, facial expression, etc. But, it comes with the automatic generation of robotic behaviors that makes sure of reaching a credible decision for the 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 the 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) the spontaneous establishment of ad-hoc missions in connection to perceptual goals, 2) the determination of an optimal set of evidences to be selected and/or collected for processing based on in-situ monitoring of the current situation, 3) the integration of such behavioral building blocks as mission management, evidence selection, evidence collection, evidence fusion and filtering for decision-making in an asynchronous and concurrent architecture, and 4) the implementation of behavioral personality of a robot under CRE framework.