Page 7 - Rapid Learning in Robotics

Contents

Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
Acknowledgment . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
Table of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

1 Introduction 1

2 The Robotics Laboratory 9
2.1 Actuation: The Puma Robot . . . . . . . . . . . . . . . . . . . 9
2.2 Actuation: The Hand “Manus” . . . . . . . . . . . . . . . . . 16
2.2.1 Oil model . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.2.2 Hardware and Software Integration . . . . . . . . . . 17
2.3 Sensing: Tactile Perception . . . . . . . . . . . . . . . . . . . . 19
2.4 Remote Sensing: Vision . . . . . . . . . . . . . . . . . . . . . . 21
2.5 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . 22

3 Artificial Neural Networks 23
3.1 A Brief History and Overview of Neural Networks . . . . . 23
3.2 Network Characteristics . . . . . . . . . . . . . . . . . . . . . 26
3.3 Learning as Approximation Problem . . . . . . . . . . . . . . 28
3.4 Approximation Types . . . . . . . . . . . . . . . . . . . . . . . 31
3.5 Strategies to Avoid Over-Fitting . . . . . . . . . . . . . . . . . 35
3.6 Selecting the Right Network Size . . . . . . . . . . . . . . . . 37
3.7 Kohonen's Self-Organizing Map . . . . . . . . . . . . . . . . 38
3.8 Improving the Output of the SOM Schema . . . . . . . . . . 41

4 The PSOM Algorithm 43
4.1 The Continuous Map . . . . . . . . . . . . . . . . . . . . . . . 43
4.2 The Continuous Associative Completion . . . . . . . . . . . 46

J. Walter “Rapid Learning in Robotics” v