A Novel Architectural Method for Producing Dynamic Gaze Behavior in Human-Robot Interactions

Abstract

We present a novel integration between a computational framework for modeling attention-driven perception and cognition (ARCADIA) with a cognitive robotic architecture (DIARC), demonstrating how this integration can be used to drive the gaze behavior of a robotic platform. Although some previous approaches to controlling gaze behavior in robots during human-robot interactions have relied either on models of human visual attention or human cognition, ARCADIA provides a novel framework with an attentional mechanism that bridges both lower-level visual and higher-level cognitive processes. We demonstrate how this approach can produce more natural and human-like robot gaze behavior. In particular, we focus on how our approach can control gaze during an interactive object learning task. We present results from a pilot crowdsourced evaluation that investigates whether the gaze behavior produced during this task increases confidence that the robot has correctly learned each object.