| Description |
xi, 563 pages : illustrations ; 24 cm |
| Series |
Kluwer international series in engineering and computer science |
|
Kluwer international series in engineering and computer science.
|
| Contents |
Machine derived contents note: Foreword. Introduction. Section I: Trajectory Generation. 1. Learning Global Topological Properties of Robot Kinematic Mapping for Neural Network-Based Configuration Control. 2. A One-Eyed Self Learning Robot Manipulator. 3. A CMAC Neural Network for the Kinematic Control of Walking Machine. 4. Neurocontroller Selective Learning from Man-in-the-Loop Feedback Control Actions. 5. Application of Self-Organizing Neural Networks for Mobile Robot Environment. 6. A Neural Network Based Inverse Kinematics Solution in Robotics. 7. Hopefield Net Generation and Encoding of Trajectories in Contained Environment. Section II: Recurrent Networks. 8. Some Preliminary Comparisons Between a Neural Adaptive Controller and a Model Reference Adaptive Controller. 9. Stable Nonlinear System Identification Using Neural Network Models. 10. Modeling of Robot Dynamics by Neural Networks with Dynamic Neurons. 11. Neural Networks Learning Rules for Control: Uniform Dynamic Backpropagation, and the Heavy Adaptive Learning Rule. 12. Parameter Learning and Compliance Control Using Neural Networks. 13. Generalisation and Extension of Motor Programs for a sequential Recurrent Network. 14. Temporally Continuous vs. Clocked Networks. Section III: Hybrid Controllers. 15. Fast Sensorimotor Skill Acquisition Based on Rule-Based Training of Neural Nets. 16. Control of Grasping in Robot Hands by Neural Networks and Expert Systems. 17. Robot Task Planning Using a Connectionist/Symbolic System. Section IV: Sensing. 18. Senses, Skills, Reactions and Reflexes Learning Automatic Behaviors in Multi-Sensory Robotic Systems. 19. A New Neural Net Approach to Robot 3D Perception and Visuo-Motion Coordination. 20. Connectivity Graphs for Space-Variant Active Vision. 21. Competitive Learning for Color Space Division. 22. Learning to Understand and Control in a World of Events. 23. Self-Selection of Input Stimuli for Improving Performance. Section V: Biological Systems. 24. A Biologically-Inspired Architecture for Reactive Motor Control. 25. Equilibria Dynamics of a Neural Network Model for Opponent Muscle Control. 26. Developmental Robotics -- A New Approach to the Specification of Robot Programs. 27. A Kinematics and Dynamics Robot Control System Based on Cerbro-Cerebellar Interaction Modelling. 28. What Frogs' Brains Tell Robots' Schemas. 29. Modulation of Robotic Motor Synergies Using Reinforcement Learning Optimization. 30. Using Optimal Control to Model Trajectory Formation and Perturbation Response in a Prehension Task. Index |
| Notes |
"Most of the papers contained in this book were presented at the First Workshop on Neural Networks in Robotics, sponsored by University of Southern California's Center for Neural Engineering, October 1991." |
| Bibliography |
Includes bibliographical references and index |
| Subject |
Neural networks (Computer science)
|
|
Neural networks (Computer science) -- Congresses.
|
|
Robots -- Control systems -- Congresses.
|
|
Robots -- Control systems.
|
| Genre/Form |
Conference papers and proceedings.
|
| Author |
Bekey, George A., 1928-
|
|
Goldberg, Ken.
|
|
University of Southern California. Center for Neural Engineering.
|
|
Workshop on Neural Networks in Robotics (1st : 1991 : Los Angeles, Calif.)
|
| LC no. |
92027162 |
| ISBN |
079239268X |
|
