Murphy: A neurally-inspired connectionist approach to learning and performance in vision-based robot motion planning

Abstract

Many aspects of intelligent animal behavior require an understanding of the complex spatial relationships between the body and its parts and the coordinate systems of the external world. This thesis deals specifically with the problem of guiding a multi-link arm to a visual target in the presence of obstacles. A simple vision-based kinematic controller and motion planner based on a connectionist network architecture has been developed, called MURPHY. The physical setup consists of a video camera and a Rhino XR-3 robot arm with three joints that move in the image plane of the camera. We assume no a priori model of arm kinematics or of the imaging characteristics of the camera/visual system, and no sophisticated built-in algorithms for obstacle avoidance. Instead, MURPHY builds a model of his arm through a combination of physical and practice, and then uses simple heuristic search with mental images of his arm to solve visually-guided reaching problems in the presence of obstacles whose traditional algorithmic solutions are extremely complex. MURPHY differs from previous approaches to robot motion-planning primarily in his use of an explicit full-visual-field representation of the workspace. Several other aspects of MURPHY's design are unusual, including the sigma-pi synaptic learning rule, the teacherless training paradigm, and the integration of sequential control within an otherwise connectionist architecture. In concluding sections we outline a series of strong correspondences between the representations and algorithms used by MURPHY, and the psychology, physiology, and neural bases for the programming and control of directed, voluntary arm movements in humans and animals.