A PARALLEL DISTRIBUTED COGNITIVE CONTROL SYSTEM FOR A HUMANOID ROBOT

Abstract

During the last decade, researchers at Vanderbilt have been developing a humanoid robot called the Intelligent Soft Arm Control (ISAC). This paper describes ISAC in terms of its software components and with respect to the design philosophy that has evolved over the course of its development. Central to the control system is a parallel, distributed software architecture, comprising a set of independent software objects or agents that execute as needed on standard PCs linked via Ethernet. Fundamental to the design philosophy is the direct physical interaction of the robot with people. Initially, this philosophy guided application development. Yet over time it became apparent that such interaction may be necessary for the acquisition of intelligent behaviors by an agent in a human-centered environment. Concurrent to that evolution was a shift from a programmer's high-level specification of action toward the robot's own motion acquisition of primitive behaviors through sensory-motor coordination (SMC) and task learning through cognitive control and working memory. Described is the parallel distributed cognitive control architecture and the advantages and limitations that have guided its development. Primary structures for sensing, memory, and cognition are described. Motion learning through teleoperation and fault diagnosis through system health monitoring are also covered. The generality of the control system is discussed in terms of its applicability to physically heterogeneous robots and multi-robot systems.