Content

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   1 Introduction
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   1.1 Objectives and contributions
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   1.2 Thesis outline
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  2 State of the art
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  2.1 Learning
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   2.1.1 Supervised
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   2.1.2 Unsupervised
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   2.1.3 Reward-based
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   2.2 Imitation
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  2.2.1 Biological background
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   2.2.1.1 Categorizing imitation
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   2.2.1.2 Imitation and memetics
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   2.2.1.3 Imitation in biology
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  2.2.2 Imitation in robotics
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   2.2.2.1 Challenges in robot imitation
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   2.2.2.2 Programming by demonstration
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   2.2.2.3 Imitation in multi-robot systems
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   2.2.3 Contrasting the thesis to the state of the art approaches
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   3 Architecture for learning and imitating in groups
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   3.1 Architectural overview
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   3.1.1 Motivation layer
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   3.1.2 Strategy layer
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   3.1.3 Skill layer
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   3.2 Layer interaction
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   3.3 Imitation in robot groups
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   3.4 Choice of the imitatee
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   3.5 Scenarios
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  4 Motivation layer
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   4.1 Background
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   4.1.1 Motivation in biological autonomous systems
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   4.1.2 Use of motivation in robots
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   4.2 Design of a robotic motivation system
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   4.2.1 Excitation
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   4.2.2 Prioritizing goals
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   4.3 Conclusion
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   5 Strategy layer
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  5.1 Background
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   5.1.1 Markov decision processes
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   5.1.1.1 Policy
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   5.1.1.2 Solving Markov decision processes
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   5.1.2 Semi-Markov decision processes
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  5.2 State of the art
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   5.2.1 Model-free approaches
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   5.2.2 Model-based approaches
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   5.2.3 Discussion
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   5.3 Policy
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   5.4 State abstraction
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  5.5 Model
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   5.5.1 Transition heuristic
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   5.5.2 Failure heuristic
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   5.5.3 Reward heuristic
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   5.5.4 Simplification heuristic
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   5.5.5 Experience heuristic
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   5.6 Sample frequency
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   5.7 Exploration
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   5.8 Example
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   6 Skill layer
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  6.1 Two modes of operation
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   6.1.1 Exploration mode
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   6.1.2 Exploitation mode
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   6.1.3 Interface with the environment
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   6.2 Component description
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   6.2.1 Skill manager
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   6.2.1.1 Skill generation
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   6.2.1.2 Skill ranking
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   6.2.1.3 Skill notification
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  6.2.2 Model manager
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   6.2.2.1 Creating and updating models
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   6.2.2.2 Scoring models
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   6.2.3 Error minimizer
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   6.3 Configuration
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   6.4 Conclusion
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  7 An integrative example
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   7.1 Implementation of the motivation layer
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   7.2 Implementation of the strategy layer
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   7.3 Implementation of the skill layer
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   7.4 Evaluation
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  8 Imitation in robot groups
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   8.1 Related work
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   8.2 Overview of the multi-robot imitation approach
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   8.3 Transforming observations
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   8.4 Understanding observed behavior
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   8.4.1 Viterbi
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   8.4.2 Interpreting observed behavior
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   8.4.3 Example
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   8.5 Integrating recognized behavior
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   8.6 Evaluation
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   8.6.1 CTF with three bases
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   8.6.2 CTF with five bases
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   8.7 Conclusion
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   9 Choice of the imitatee
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   9.1 Related work
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  9.2 Background
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   9.2.1 Bayesian networks and how to learn them
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   9.2.2 Affordances
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   9.3 Overview of the demonstrator choice process
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   9.4 Affordance detection
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   9.5 Affordance network generation
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  9.6 Comparing affordance networks
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   9.6.1 Structural difference of affordance networks
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   9.6.2 Parameter difference of affordance networks
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   9.6.3 Affordance network distance metric
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   9.7 Evaluation
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  9.7.1 Experimental setup
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   9.7.1.1 Parameterization of the environment
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   9.7.1.2 Affordances and their validation
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   9.7.1.3 Imitated behavior and how to measure its success
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  9.7.2 Selection experiment
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   9.7.2.1 Scenario
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   9.7.2.2 Procedure
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   9.7.2.3 Result
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  9.7.3 Robustness experiment
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   9.7.3.1 Scenario
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   9.7.3.2 Procedure
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   9.7.3.3 Result
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   9.7.4 Clustering experiment
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   9.7.4.1 Scenario
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   9.7.4.2 Procedure
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   9.7.4.3 Results
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   9.8 Conclusion
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   10 Summary and outlook
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   10.1 Summary
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   10.2 Contributions
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   10.3 Outlook
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   A Notation
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   B Algorithms
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   List of Figures
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   List of Tables
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   Own publications
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   Bibliography