MURPHY is a vision-based kinematic controller and path planner based on a connectionist architecture, and implemented with a video camera and Rhino XR-series robot arm. Imitative of the layout of sen(cid:173) sory and motor maps in cerebral cortex, MURPHY'S internal representa(cid:173) tions consist of four coarse-coded populations of simple units represent(cid:173) ing both static and dynamic aspects of the sensory-motor environment. In previously reported work [4], MURPHY first learned a direct kinematic model of his camera-arm system during a period of extended practice, and then used this "mental model" to heuristically guide his hand to unobstructed visual targets. MURPHY has since been extended in two ways: First, he now learns the inverse differential-kinematics of his arm in addition to ordinary direct kinematics, which allows him to push his hand directly towards a visual target without the need for search. Sec(cid:173) ondly, he now deals with the much more difficult problem of reaching in the presence of obstacles.

Secondly, he now deals with the much more difficult problem of reaching in the presence of obstacles.

Secondly, he now deals with the much more difficult problem of reaching in the presence of obstacles.

Visual guidance of a multi-link arm through a cluttered workspace is known to be an extremely difficult computational problem. Classical approaches in the field of robotics have typically broken the problem into pieces of manageable size, including modules for direct and inverse kinematics and dynamics [7], along with a variety of highly complex algorithms for motion planning in the configuration space of a multi-link arm (e.g.