Robot Learning

1 Introduction to Robot Learning. - 1 Motivation. - 2 The Robot Learning Problem. - 3 Background. - 4 Domains. - 5 Roadmap. - 2 Knowledge-based Training of Artificial Neural Networks for Autonomous Robot Driving. - 1 Introduction. - 2 Network Architecture. - 3 Network Training. - 4 Performance Improvement Using Transformations. - 5 Results and Comparison. - 6 Discussion. - 3 Learning Multiple Goal Behavior via Task Decomposition and Dynamic Policy Merging. - 1 Introduction. - 2 Basics of Reinforcement Learning. - 3 Multiple Goal Tasks. - 4 A Decomposition Approach. - 5 Search-based Merging. - 6 A Hybrid Architecture. - 7 Summary. - 4 Memory-based Reinforcement Learning:Converging with Less Data and Less Real Time. - 1 Introduction. - 2 Prioritized Sweeping. - 3 A Markov Prediction Experiment. - 4 Learning Control of Markov Decision Tasks. - 5 Experimental Results. - 6 Discussion. - 7 Conclusion. - 5 Rapid Task Learning for Real Robots. - 1 Introduction. - 2 Behavior-based Reinforcement Learning. - 3 Exploiting Local Spatial Structure. - 4 Using Action Models. - 5 Highly Structured Learning. - 6 Summary. - 6 The Semantic Hierarchy in Robot Learning. - 1 Introduction. - 2 The Cognitive Map and the Semantic Hierarchy. - 3 From Simulated Robot to Physical Robots. - 4 From Tabula Rasa to Cognitive Mapping. - 5 From Low-Speed to High-Speed Motion. - 6 Conclusions. - 7 Uncertainty In Graph-Based Map Learning. - 1 Introduction. - 2 Qualitative Navigation and Map Learning. - 3 Theoretical Development. - 4 Problem Classification. - 5 Summary of Results. - 6 Conclusions. - 8 Real Robots Real Learning Problems. - 1 Introduction. - 2 Motivation. - 3 The Main Types of Learning. - 4 The Main Methods of Learning. - 5 Simulation. - 6 Conclusion. Language: English