كتاب Rapid Learning in Robotics

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Rapid Learning in Robotics - Jorg Walter

ويتناول الموضوعات الأتية :

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
4.3 The Best-Match Search 51
4.4 Learning Phases 53
4.5 Basis Function Sets, Choice and Implementation Aspects 56
4.6 Summary 60
5 Characteristic Properties by Examples 63
5.1 Illustrated Mappings – Constructed From a Small Number of Points 63
5.2 Map Learning with Unregularly Sampled Training Points 66
5.3 Topological Order Introduces Model Bias 68
5.4 “Topological Defects” 70
5.5 Extrapolation Aspects 71
5.6 Continuity Aspects 72
5.7 Summary 74
6 Extensions to the Standard PSOM Algorithm 75
6.1 The “Multi-Start Technique” 76
6.2 Optimization Constraints by Modulating the Cost Function 77
6.3 The Local-PSOM 78
6.3.1 Approximation Example: The Gaussian Bell 80
6.3.2 Continuity Aspects: Odd Sub-Grid Sizes n Give Options
6.3.3 Comparison to Splines 82
6.4 Chebyshev Spaced PSOMs 83
6.5 Comparison Examples: The Gaussian Bell 84
6.5.1 Various PSOM Architectures 85
6.5.2 LLM Based Networks 87
6.6 RLC-Circuit Example 88
6.7 Summary 91
7 Application Examples in the Vision Domain 95
7.1 2D Image Completion 95
7.2 Sensor Fusion and 3D Object Pose Identification 97
7.2.1 Reconstruct the Object Orientation and Depth 97
7.2.2 Noise Rejection by Sensor Fusion 99
7.3 Low Level Vision Domain: a Finger Tip Location Finder 102
8 Application Examples in the Robotics Domain 107
8.1 Robot Finger Kinematics 107
8.2 The Inverse 6D Robot Kinematics Mapping 112
8.3 Puma Kinematics: Noisy Data and Adaptation to Sudden
Changes 118
8.4 Resolving Redundancy by Extra Constraints for the Kinematics
8.5 Summary 123
9 “Mixture-of-Expertise” or “Investment Learning” 125
9.1 Context dependent “skills” 125
9.2 “Investment Learning” or “Mixture-of-Expertise” Architecture
9.2.1 Investment Learning Phase 127
9.2.2 One-shot Adaptation Phase 128
9.2.3 “Mixture-of-Expertise” Architecture 128
9.3 Examples 130
9.3.1 Coordinate Transformation with and without Hierarchical
PSOMs 131
9.3.2 Rapid Visuo-motor Coordination Learning 132
9.3.3 Factorize Learning: The 3D Stereo Case 136
10 Summary 139
Bibliography 146

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