Connectionist Robot Motion Planning: A Neurally-Inspired Approach to Visually-Guided Reaching is the third series in a cluster of books on robotics and related areas as part of the Perspectives in Artificial Intelligence Series. This series focuses on an experimental paradigm using the MURPHY system to tackle critical issues surrounding robot motion planning. MURPHY is a robot-camera system developed to explore an approach to the kinematics of sensory-motor learning and control for a multi-link arm. Organized into eight chapters, this book describes the guiding of a multi-link arm to visual targets in a cluttered workspace. It primarily focuses on “ecological? solutions that are relevant to the typical visually guided reaching behaviors of humans and animals in natural environments. Algorithms that work well in unmodeled workspaces whose effective layouts can change from moment to moment with movements of the eyes, head, limbs, and body are also presented. This book also examines the strengths of neurally inspired connectionist representations and the utility of heuristic search when good performance, even if suboptimal, is adequate for the task. The co-evolution of MURPHY’s design with the brain, presumably in response to similar computational pressures, is described in the concluding chapters, specifically presenting the division of labor between programmed-feedforward and visual-feedback modes of limb control. Design engineers in the fields of biology, neurophysiology, and cognitive psychology will find this book of great value.

Table of Contents:
Preface Acknowledgments 1 Introduction 2 MURPHY's Organization The Physical Setup The Connectionist Architecture Representational Choices MURPHY's Kinematics Traditional Methods Connectionist Kinematics The Sequential Controller 3 How MURPHY Learns Learning by Doing Abridged History of the Idea Motivating Sigma-Pi Learning Connectionist Supervised Associative Learning A Historical Quandary New Algorithms Are More Powerful, Less Biological Changing Assumptions Learning Functions with Lookup-Tables Building Receptive Fields with Multiplication Sigma-Pi Learning The Sigma-Pi Unit The Learning Rule An Example Generalization to Novel Inputs K-d Tree Reimplementation 4 MURPHY in Action Planning with Gradient Descent Motion Planning with Obstacles Four Visual Routines The Search Procedure Discussion Visual-Feedback Control System Performance and Scaling Behavior Implementation and Performance Notes Scaling MURPHY Up 5 Robotics Issues Learning vs. Built-in Models Style of Representation Using the Full Visual Channel Styles of Robot Motion Planning Artificial Potential Fields: Local Methods Geometric Motion Planning: Global Methods Hybrid Methods: Incorporating Heuristic Search MURPHY Uses Direct Heuristic Search 6 Psychological Issues Psychoanalyzing MURPHY Development and Plasticity in Limb Control The Necessity of Active Sensory-Motor Learning Developmental Evidence for Two Modes of Control Possible Evidence for Local Learning Models, Imagery, Practice, and Stability Building and Using Mental Models Mental Imagery Mental Practice: Learning by Thinking Corollary Discharges and Perceptual Stability Summary of Psychological Issues 7 Biological Issues The Muscle Interface A Cortical Basis for Visual Limb Control A Programmed-Feedforward Neural Subsystem A Visual-Feedback Neural Subsystem Summary of Biological Issues 8 Conclusions Scientific and Engineering Lessons Learned Learning by Doing Learning with Lookup-Tables The Connectionist Architecture Mental Models, Heuristic Search Reflections on Brain and Behavior Future Directions Pragmatic Extensions Theoretical Extensions Bibliography Index