제 9 주. 응용 -4: Robotics Synthesis of Autonomous Robots through Evolution S. Nolfi and D. Floreano, Trends in Cognitive Science, vol. 6, no. 1, pp. 31~37, 2002 학습목표 Evolutionary robotics 의 세가지 성공사례를 살펴보고 그 가능 성 이해

개요 4 Evolutionary robotics –Develop robots through self-organized process based on evolution Population of genetically specified controller  genetic variations Phylogenetic and ontogenetic adaptation –Situated in a physical environment –Autonomous development of skills in close interaction with the environment 4 3 research topics on synthesis and study of robots –Exploit opportunities to interact with external environment –Consist of control system and body morphology –Display ability to adapt to varying environmental conditions

Interaction with Environment 4 Basic idea –Behavior emerges from dynamical interaction between nervous system, body and external environment –Motor actions partially determine sensory pattern that organisms receive from the environment 4 Robots autonomously develop skills in close interaction with environment –Ideal framework for studying sensory-motor coordination –Other ways to exploit sensory-motor coordination Increase (or decrease) frequency of sensory states to react effectively Select sensory states to help good behavior Increase perceived differences between different objects Select useful learning experiences

Co-evolution of Body and Brain 4 Cliff et al. and Harvey et al. –Genotype consists of two parts encoding Control system: connection weights and architecture of neural controller Visual morphology: number, size and position of visual receptive fields 4 Lichtensteiger and Eggenberger 4 Lund et al. 4 Pollack and co-workers

Evolution of Plastic Individuals 4 Definition –Individuals that are also capable of adapting during lifetime 4 Lifetime adaptation –Complement evolution by allowing individuals to adapt to environmental changes –Help and guide evolution by channelling the evolutionary process towards promising directions  speed up –Produce more effective behaviors  scale up

Conclusion 4 Artificial evolution is an ideal framework for the synthesis and study of systems whose behaviors emerge from the interaction between the control system, body and the environment 4 Contribution to understand natural systems –Experiments conducted by evolving robots in specific environmental conditions can help us to understand how natural organisms solve similar problems in similar environmental conditions –Evolutionary robotics experiments may allow us to understand general principles that regulate natural systems 4 Drawbacks of evolutionary robotics –Amount of time needed to conduct an evolutionary process on physical robots –Identification of methods of encoding information into genotype that are suitable to produce incremental and open-ended evolution