Graphical Models for Integrated Intelligent Robot Architectures

Abstract

The theoretically elegant yet broadly functional capability of graphical models shows intriguing potential to span in a uniform manner perception, cognition and action; and thus to ultimately yield simpler yet more powerful integrated architectures for intelligent robots and other comparable systems. This position paper explores this potential, with initial support from an effort underway to develop a graphical architecture that is based on factor graphs (with piecewise continuous functions). Robots require a close coupling of (multiple forms of) perception and action. Intelligent robots go beyond this to require a further coupling with cognition. From the perspective of robotics – with its focus on behavior in the world – the construction of intelligent robots generally emphasizes a tightly integrated perceptuomotor system that is then loosely connected to some limited form of cognitive system (such as a planner); as for example in (Bonasso et al. 1997). From the perspective of cognitive architectures – with their focus on integrated embodiments of hypotheses concerning the fixed structure underlying intelligent behavior – the construction of intelligent robots generally emphasizes a highly functional cognitive system that is then loosely connected to limited perceptual and motor modules; as for example in (Laird and Rosenbloom 1990). Neither perspective typically strives for a deep integration across the signal-to-symbol divide that separates the perceptuomotor and cognitive systems, nor even to do full justice to what is on the other side. Other approaches are possible though. One such is a form of graphical architecture that leverages the broadly functional yet theoretically elegant construct of graphical models (Koller and Friedman 2009) to support, among other things, a uniform approach to signal and symbol