
Connectionist Robot Motion Planning
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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
Product details
- No. of pages: 182
- Language: English
- Copyright: © Academic Press 1990
- Published: August 28, 1990
- Imprint: Academic Press
- eBook ISBN: 9780323141260
About the Author
Bartlett Mel
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