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Neural Networks in Robotics : The Springer International Series in Engineering and Computer Science - George A. Bekey

Neural Networks in Robotics

The Springer International Series in Engineering and Computer Science

By: George A. Bekey (Editor), Kenneth Y. Goldberg (Editor)


Published: 30th November 1992
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Neural Networks in Robotics is the first book to present an integrated view of both the application of artificial neural networks to robot control and the neuromuscular models from which robots were created. The behavior of biological systems provides both the inspiration and the challenge for robotics. The goal is to build robots which can emulate the ability of living organisms to integrate perceptual inputs smoothly with motor responses, even in the presence of novel stimuli and changes in the environment. The ability of living systems to learn and to adapt provides the standard against which robotic systems are judged. In order to emulate these abilities, a number of investigators have attempted to create robot controllers which are modelled on known processes in the brain and musculo-skeletal system. Several of these models are described in this book. On the other hand, connectionist (artificial neural network) formulations are attractive for the computation of inverse kinematics and dynamics of robots, because they can be trained for this purpose without explicit programming. Some of the computational advantages and problems of this approach are also presented. For any serious student of robotics, Neural Networks in Robotics provides an indispensable reference to the work of major researchers in the field. Similarly, since robotics is an outstanding application area for artificial neural networks, Neural Networks in Robotics is equally important to workers in connectionism and to students for sensormonitor control in living systems.

` Given the potential of neural networks to reduce the design complexity of control systems through learning, this book is essential for those involved in the research and development of robotic control systems. ' University Computing, Vol.15/4, 1993

Trajectory Generationp. 1
Learning Global Topological Properties of Robot Kinematic Mappings for Neural Network-based Configuration Controlp. 3
A One-eyed Self Learning Robot Manipulatorp. 19
A CMAC Neural Network for the Kinematic Control of Walking Machinep. 29
Neurocontroller Selective Learning from Man-in-the-Loop Feedback Control Actionsp. 45
Application of Self-Organizing Neural Networks for Mobile Robot Environmentp. 85
A Neural Network Based Inverse Kinematics Solution in Roboticsp. 97
Hopefield Net Generation and Encoding of Trajectories in Contained Environmentp. 113
Recurrent Networksp. 129
Some Preliminary Comparisons Between a Neural Adaptive Controller and a Model Reference Adaptive Controllerp. 131
Stable Nonlinear System Identification Using Neural Network Modelsp. 147
Modeling of Robot Dynamics by Neural Networks with Dynamic Neuronsp. 165
Neural Networks Learning Rules for Control: Uniform Dynamic Backpropagation, and the Heavy Adaptive Learning Rulep. 177
Parameter Learning and Compliance Control Using Neural Networksp. 193
Generalisation and Extension of Motor Programs for a Sequential Recurrent Networkp. 217
Temporally Continuous vs. Clocked Networksp. 237
Hybrid Controllersp. 253
Fast Sensorimotor Skill Acquisition Based on Rule-Based Training of Neural Netsp. 255
Control of Grasping in Robot Hands by Neural Networks and Expert Systemsp. 271
Robot Task Planning Using a Connectionist/Symbolic Systemp. 295
Sensingp. 317
Senses, Skills, Reactions and Reflexes Learning Automatic Behaviors in Multi-sensory Robotic Systemsp. 319
A New Neural Net Approach to Robot 3D Perception and Visuo-Motion Coordinationp. 331
Connectivity Graphs for Space-Variant Active Visionp. 349
Competitive Learning for Color Space Divisionp. 375
Learning to Understand and Control in a World of Eventsp. 389
Self-selection of Input Stimuli for Improving Performancep. 403
Biological Systemsp. 419
A biologically-Inspired Architecture for Reactive Motor Controlp. 421
Equilibria Dynamics of a Neural Network Model for Opponent Muscle Controlp. 439
Developmental Robotics' - A New Approach to the Specification of Robot Programsp. 459
A Kinematics and Dynamics Robot Control System Based on Cerbro-cerebellar Interaction Modellingp. 487
What Frogs' Brains Tell Robots' Schemasp. 503
Modulation of Robotic Motor Synergies Using Reinforcement Learning Optimizationp. 521
Using Optimal Control to Model Trajectory Formation and Perturbation Response in a Prehension Taskp. 539
Indexp. 559
Table of Contents provided by Blackwell. All Rights Reserved.

ISBN: 9780792392682
ISBN-10: 079239268X
Series: The Springer International Series in Engineering and Computer Science
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 563
Published: 30th November 1992
Publisher: Springer
Country of Publication: NL
Dimensions (cm): 23.5 x 15.5  x 3.18
Weight (kg): 2.17