Description: 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.

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