Foreword | |
Introduction | |
Acknowledgements | |
Trajectory Generation | p. 1 |
Learning Global Topological Properties of Robot Kinematic Mappings for Neural Network-based Configuration Control | p. 3 |
A One-eyed Self Learning Robot Manipulator | p. 19 |
A CMAC Neural Network for the Kinematic Control of Walking Machine | p. 29 |
Neurocontroller Selective Learning from Man-in-the-Loop Feedback Control Actions | p. 45 |
Application of Self-Organizing Neural Networks for Mobile Robot Environment | p. 85 |
A Neural Network Based Inverse Kinematics Solution in Robotics | p. 97 |
Hopefield Net Generation and Encoding of Trajectories in Contained Environment | p. 113 |
Recurrent Networks | p. 129 |
Some Preliminary Comparisons Between a Neural Adaptive Controller and a Model Reference Adaptive Controller | p. 131 |
Stable Nonlinear System Identification Using Neural Network Models | p. 147 |
Modeling of Robot Dynamics by Neural Networks with Dynamic Neurons | p. 165 |
Neural Networks Learning Rules for Control: Uniform Dynamic Backpropagation, and the Heavy Adaptive Learning Rule | p. 177 |
Parameter Learning and Compliance Control Using Neural Networks | p. 193 |
Generalisation and Extension of Motor Programs for a Sequential Recurrent Network | p. 217 |
Temporally Continuous vs. Clocked Networks | p. 237 |
Hybrid Controllers | p. 253 |
Fast Sensorimotor Skill Acquisition Based on Rule-Based Training of Neural Nets | p. 255 |
Control of Grasping in Robot Hands by Neural Networks and Expert Systems | p. 271 |
Robot Task Planning Using a Connectionist/Symbolic System | p. 295 |
Sensing | p. 317 |
Senses, Skills, Reactions and Reflexes Learning Automatic Behaviors in Multi-sensory Robotic Systems | p. 319 |
A New Neural Net Approach to Robot 3D Perception and Visuo-Motion Coordination | p. 331 |
Connectivity Graphs for Space-Variant Active Vision | p. 349 |
Competitive Learning for Color Space Division | p. 375 |
Learning to Understand and Control in a World of Events | p. 389 |
Self-selection of Input Stimuli for Improving Performance | p. 403 |
Biological Systems | p. 419 |
A biologically-Inspired Architecture for Reactive Motor Control | p. 421 |
Equilibria Dynamics of a Neural Network Model for Opponent Muscle Control | p. 439 |
Developmental Robotics' - A New Approach to the Specification of Robot Programs | p. 459 |
A Kinematics and Dynamics Robot Control System Based on Cerbro-cerebellar Interaction Modelling | p. 487 |
What Frogs' Brains Tell Robots' Schemas | p. 503 |
Modulation of Robotic Motor Synergies Using Reinforcement Learning Optimization | p. 521 |
Using Optimal Control to Model Trajectory Formation and Perturbation Response in a Prehension Task | p. 539 |
Index | p. 559 |
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