| Introduction | p. 1 |
| Concepts of Symbiotic Robot Organisms | p. 5 |
| From Robot Swarm to Artificial Organisms: Self-organization of Structures, Adaptivity and Self-development | p. 5 |
| Mono- and Multi- functional Artificial Self-organization | p. 7 |
| Collective Robotics: Problem of Structures | p. 11 |
| Adaptability and Self-development | p. 14 |
| Artificial Symbiotic Systems: Perspectives and Challenges | p. 21 |
| Towards a Synergetic Quantum Field Theory for Evolutionary, Symbiotic Multi-Robotics | p. 25 |
| Cooperative (Coherent) Operations between Fermionic Units | p. 28 |
| Individual Contributions of the Eigenanteile | p. 36 |
| Separate Perturbations of the Eigenanteile | p. 40 |
| Coupling of the Disturbed Eigenanteil Equations | p. 42 |
| Information Model and Interactions of Structured Components | p. 45 |
| Functional and Reliability Modelling of Swarm Robotic Systems | p. 54 |
| Macroscopic Probabilistic Modelling in Swarm Robotics | p. 54 |
| Reliability Modelling of Swarm Robotic Systems | p. 65 |
| Concluding Discussion | p. 76 |
| Heterogeneous Multi-Robot Systems | p. 79 |
| Reconfigurable Heterogeneous Mechanical Modules | p. 79 |
| A Heterogeneous Approach in Modular Robotics | p. 80 |
| Integration and Miniaturization | p. 82 |
| Locomotion Mechanisms | p. 84 |
| Docking Mechanisms and Strategies | p. 86 |
| Mechanical Degrees of Freedoms: Actuation for the Individual Robot and for the Organism | p. 88 |
| Tool Module: Active Wheel | p. 88 |
| Summary of the Three Robotic Platforms | p. 91 |
| Computation, Distributed Sensing and Communication | p. 92 |
| Electronic Architectures in Related Works | p. 93 |
| General Hardware Architecture in Symbrion/Replicator | p. 94 |
| General Sensor Capabilities | p. 97 |
| Vision and IR-Based Perception | p. 100 |
| Triangulation Laser Range Sensor for Obstacle Detection and Interpretation of Basic Geometric Features | p. 105 |
| Powerful Wireless Communication and 3D Real Time Localisation Systems | p. 107 |
| Integration Issues | p. 113 |
| Energy Autonomy and Energy Harvesting in Reconfigurable Swarm Robotics | p. 114 |
| Energy Autonomy | p. 115 |
| Energy Harvesting | p. 116 |
| Energy Trophallaxis | p. 119 |
| Energy Sharing within a Robot Organism | p. 121 |
| Energy Management | p. 122 |
| Modular Robot Simulation | p. 133 |
| Simulation Environments | p. 134 |
| The Symbricator3D Simulation Environment | p. 137 |
| Showcase: The Dynamics Predictor | p. 149 |
| Conclusion and Future Work | p. 162 |
| Cognitive Approach in Artificial Organisms | p. 165 |
| Cognitive World Modeling | p. 165 |
| Methodology | p. 166 |
| Spatial World Modeling | p. 166 |
| Evolution Map | p. 167 |
| Map | p. 169 |
| Jockeys | p. 170 |
| Reasoning | p. 172 |
| Executor | p. 173 |
| Porting the EMa onto a Robot | p. 174 |
| EMa Care-Taking Procedures | p. 175 |
| Physical Layout | p. 176 |
| Logical Layout and Communication | p. 177 |
| Experiments | p. 179 |
| Functional World Modelling | p. 180 |
| Emergent Cognitive Sensor Fusion | p. 183 |
| Scenarios | p. 185 |
| Towards Embodied and Emergent Cognition | p. 188 |
| Sensor Fusion Model | p. 192 |
| Application of Embodied Cognition to the Development of Artificial Organisms | p. 202 |
| Natural vs. Artificial Systems: Collectivity and Adaptability in Inanimated Nature | p. 203 |
| Definition of Information and Knowledge Related to Restrictions | p. 211 |
| Collectivity and Adaptability in Animated Nature | p. 219 |
| Information Based Learning to Develop and Maintain Artificial Organisms | p. 221 |
| Adaptive Control Mechanisms | p. 229 |
| General Controller Framework | p. 229 |
| Controller Framework in Symbrion/Replicator | p. 229 |
| Bio-inspiration for the Structure of Artificial Genome | p. 232 |
| Action Selection Mechanism | p. 234 |
| Overview of Different Control Mechanisms | p. 235 |
| Hormone-Based Control for Multi-modular Robotics | p. 240 |
| Micro-organisms' Cell Signals and Hormones as Source of Inspiration | p. 241 |
| Related Work | p. 246 |
| Artificial Homeostatic Hormone System (AHHS) | p. 247 |
| Encoding an AHHS into a Genome | p. 249 |
| Self-organised Compartmentalisation | p. 250 |
| Evolutionary Adaptation | p. 255 |
| Single Robots | p. 256 |
| Forming Robot Organisms | p. 257 |
| Locomotion of Robot Organisms | p. 259 |
| Feedbacks | p. 261 |
| Conclusion | p. 262 |
| Evolving Artificial Neural Networks and Artificial Embryology | p. 263 |
| Shaping of ANN in Literature | p. 264 |
| Overview over Section | p. 266 |
| Concept of Adapting Virtual Embryogenesis for Controller Development | p. 266 |
| Diffusion Processes | p. 267 |
| Genetics and Cellular Behaviour | p. 268 |
| Simulated Physics | p. 269 |
| Cell Specialisation | p. 270 |
| Linkage | p. 270 |
| Depicting Genetic Structures and Feedbacks | p. 272 |
| Stable Growth due to Feedbacks in Genetic Structure | p. 275 |
| Developing Complex Shapes | p. 276 |
| The Growth of Neurons | p. 277 |
| Translation | p. 278 |
| Usability of Virtual Embryogenesis in the Context of Artificial Evolution for Shaping Artificial Neural Networks and Robot Controllers | p. 279 |
| Subsumption of Section | p. 281 |
| An Artificial Immune System for Robot Organisms | p. 282 |
| A Biological and Engineering Perspective | p. 283 |
| An Immune-inspired Architecture for Fault Tolerance in Swarm and Collective Robotic Systems | p. 290 |
| Innate Layer | p. 293 |
| Adaptive Layer | p. 294 |
| Summary | p. 305 |
| Structural Self-organized Control | p. 306 |
| Representation of Structures | p. 308 |
| Compact Representation: The Topology Generator | p. 313 |
| Scalability of Structures and Appearing Constraints | p. 314 |
| Morphogenesis as an Optimal Decision Problem | p. 317 |
| Self-organized Morphogenesis | p. 322 |
| Collective Memory and Further Points | p. 325 |
| Kinematics and Dynamics for Robot Organisms | p. 326 |
| Modeling of Multi-robot Organisms | p. 328 |
| Inverse Kinematics | p. 332 |
| Dynamics | p. 333 |
| Computational Analysis | p. 335 |
| Conclusion | p. 336 |
| Learning, Artificial Evolution and Cultural Aspects of Symbiotic Robotics | p. 337 |
| Machine Learning for Autonomous Robotics | p. 337 |
| Related Work | p. 338 |
| Challenges for ML-Based Robotics | p. 347 |
| The WOALA Scheme | p. 349 |
| First Experiments with WOALA | p. 353 |
| Discussion and Perspectives | p. 361 |
| Embodied, On-Line, On-Board Evolution for Autonomous Robotics | p. 362 |
| Controllers, Genomes, Learning, and Evolution | p. 363 |
| Classification of Approaches to Evolving Robot Controllers | p. 364 |
| The Classical Off-Line Approach Based on a Master EA | p. 368 |
| On-Line Approaches | p. 369 |
| Testing Encapsulated Evolutionary Approaches | p. 372 |
| Conclusions and Future Work | p. 382 |
| Artificial Sexuality and Reproduction of Robots Organisms | p. 384 |
| The Role of Sexuality for Robots | p. 385 |
| Artificial Reproduction | p. 388 |
| Implementation of Artificial Sexuality on Real Robots | p. 390 |
| Evolutionary Engineering | p. 392 |
| Evolution of Multicellular Organisms | p. 397 |
| Sex and Reproduction of Symbiotic Robots | p. 399 |
| Conclusion | p. 403 |
| Self-learning Behavior of Virus-Like Artificial Organisms | p. 403 |
| Effectiveness of Evolutionary Optimization for Genetic Cloud | p. 405 |
| Interaction between Evolution and Learning in an Evolutionary Process | p. 412 |
| Evolutionary Emergence of a Cooperation between Agents | p. 418 |
| Discovering of Chains of Actions by Self-learning Agents | p. 421 |
| Virus-Like Organisms: New Adaptive Paradigm? | p. 424 |
| Towards the Emergence of Artificial Culture in Collective Robotic Systems | p. 425 |
| Project Aims | p. 425 |
| The Artificial Culture Laboratory | p. 426 |
| The Challenges and the Case for an Emerging Robot Culture | p. 428 |
| Robot Memes and Meme Tracking | p. 430 |
| Concluding Remarks | p. 433 |
| Final Conclusions | p. 435 |
| References | p. 437 |
| Index | p. 467 |
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