Kimberly A. Hambuchen

A PARALLEL DISTRIBUTED COGNITIVE CONTROL SYSTEM FOR A HUMANOID ROBOT

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 described. The generality of the control system is discussed in terms of its applicability to physically heterogeneous robots and multi-robot systems.

The sensory ego-sphere: a mediating interface between sensors and cognition

The Sensory Ego-Sphere (SES) is an interface for a robot that serves to mediate information between sensors and cognition. The SES can be visualized as a sphere centered on the coordinate frame of the robot, spatially indexed by polar and azimuthal angles. Internally, the SES is a graph with a fixed number of edges that partitions surrounding space and contains localized sensor information from the robot. This paper describes the SES and gives the results of implementing the SES on multiple robots, both humanoid and mobile, to support essential functions such as a localized short-term memory, spatio-temporal sensory-motor event detection, attentional processing, data sharing, and ego-centric navigation.

Towards a human–robot symbiotic system

Abstract Partnership between a person and a robot could be simplified if the robot were intelligent enough to understand human intentions and perform accordingly. During the last decade, we have been developing such an intelligent robot called ISAC. Originally, ISAC was designed to assist the physically disabled, but gradually became a test bed for more robust human–robot teaming (see http://eecs.vanderbilt.edu/CIS/ ). In this paper, we will describe a framework for human–robot interaction, a multi-agent based robot control architecture, and short- and long-term memory structures for the robot brain. Two applications will illustrate how ISAC interacts with the human.

Toward a Human–Robot Symbiotic System

Partnership between a person and a robot could be simplified if the robot were intelligent enough to understand human intentions and perform accordingly. During the last decade, we have been developing such an intelligent robot called Intelligent Soft-Arm Control (ISAC). Originally, ISAC was designed to assist the physically disabled, but gradually became a test bed for more robust human-robot teaming (see: http://eecs.vanderbilt.edu/CIS/ ). In this chapter, we will describe a framework for human-robot interaction, a multi-agent based robot control architecture, and short-term and long-term memory structures for the robot brain. Two applications will illustrate how ISAC interacts with humans.