Evolutionary robotics: exploiting the full power of self-organization

Evolutionary robotics approaches are based on genetic algorithms. An initial population of different “genotypes”, each codifying the control system (and possibly the morphology) of a robot, are created randomly. Each robot is evaluated in the environment and to each robot is assigned a score (fitness) that measures its ability to perform a desired task Then, the robots that have obtained the highest fitness are allowed to reproduce (sexually or agamically) by generating copies of their genotypes with the addition of changes introduced by some genetic operators (e.g. mutations, duplication, etc.). The process is repeated for a certain number of generations until, hopefully, the desired performances are achieved. The authors discuss the implications of evolutionary robotics for other disciplines. Although they think that evolutionary robotics may be relevant for many different fields, they restrict their analysis to engineering, biology, and ethology. (7 pages)

[1]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[2]  J. Krebs,et al.  Arms races between and within species , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[3]  Rodney A. Brooks,et al.  A Robust Layered Control Syste For A Mobile Robot , 2022 .

[4]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[5]  D.E. Goldberg,et al.  Classifier Systems and Genetic Algorithms , 1989, Artif. Intell..

[6]  C. Gallistel.  The organization of learning , 1990 .

[7]  Stewart W. Wilson.  The animat path to AI , 1991 .

[8]  David H. Ackley,et al.  Interactions between learning and evolution , 1991 .

[9]  Dave Cliff,et al.  Computational neuroethology: a provisional manifesto , 1991 .

[10]  P. Todd,et al.  Exploring Adaptive Agency I: Theory and Methods for Simulating the Evolution of Learning , 1991 .

[11]  Jean-Arcady Meyer,et al.  Simulation of adaptive behavior in animats: review and prospect , 1991 .

[12]  E. Rosch,et al.  The Embodied Mind: Cognitive Science and Human Experience , 1993 .

[13]  Rodney A. Brooks,et al.  Artificial Life and Real Robots , 1992 .

[14]  Inman Harvey,et al.  The SAGA Cross: The Mechanics of Recombination for Species with Variable Length Genotypes , 1992, PPSN.

[15]  Sridhar Mahadevan,et al.  Automatic Programming of Behavior-Based Robots Using Reinforcement Learning , 1991, Artif. Intell..

[16]  Inman Harvey,et al.  Explorations in Evolutionary Robotics , 1993, Adapt. Behav..

[17]  Marco Dorigo,et al.  Genetics-based machine learning and behavior-based robotics: a new synthesis , 1993, IEEE Trans. Syst. Man Cybern..

[18]  Francesco Mondada,et al.  Mobile Robot Miniaturisation: A Tool for Investigation in Control Algorithms , 1993, ISER.

[19]  Francesco Mondada,et al.  Automatic creation of an autonomous agent: genetic evolution of a neural-network driven robot , 1994 .

[20]  Marco Colombetti,et al.  Robot Shaping: Developing Autonomous Agents Through Learning , 1994, Artif. Intell..

[21]  Stefano Nolfi,et al.  How to Evolve Autonomous Robots: Different Approaches in Evolutionary Robotics , 1994 .

[22]  Inman Harvey,et al.  Seeing the Light: Artiicial Evolution, Real Vision Seeing the Light: Artiicial Evolution, Real Vision , 1994 .

[23]  Rolf Pfeifer,et al.  Classification as Sensory-Motor Coordination: A Case Study on Autonomous Agents , 1995, ECAL.

[24]  Andrew G. Barto,et al.  Learning to Act Using Real-Time Dynamic Programming , 1995, Artif. Intell..

[25]  Stefano Nolfi,et al.  Learning to Adapt to Changing Environments in Evolving Neural Networks , 1996, Adapt. Behav..

[26]  Dave Cliff,et al.  Challenges in evolving controllers for physical robots , 1996, Robotics Auton. Syst..

[27]  Marco Colombetti,et al.  Behavior analysis and training-a methodology for behavior engineering , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[28]  Francesco Mondada,et al.  Evolution of Plastic Neurocontrollers for Situated Agents , 1996 .

[29]  Marco Colombetti,et al.  Robot Shaping: An Experiment in Behavior Engineering , 1997 .

[30]  John Hallam,et al.  Evolving robot morphology , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[31]  Rolf Pfeifer,et al.  Sensory - motor coordination: The metaphor and beyond , 1997, Robotics Auton. Syst..

[32]  Inman Harvey,et al.  Evolutionary robotics: the Sussex approach , 1997, Robotics Auton. Syst..

[33]  Stefano Nolfi,et al.  Evolving non-trivial behaviors on real robots: A garbage collecting robot , 1997, Robotics Auton. Syst..

[34]  D. Parisi.  Artificial Life and Higher Level Cognition , 1997, Brain and Cognition.

[35]  Randall D. Beer,et al.  The brain has a body: adaptive behavior emerges from interactions of nervous system, body and environment , 1997, Trends in Neurosciences.

[36]  Jean-Arcady Meyer,et al.  Evolution and Development of Modular Control Architectures for 1D Locomotion in Six-legged Animats , 1998, Connect. Sci..

[37]  Neil Burgess,et al.  Using a Mobile Robot to Test a Model of the Rat Hippocampus , 1998, Connect. Sci..

[38]  Tom Ziemke,et al.  Evolution of visually-guided approach behaviour in recurrent artificial neural network robot controllers , 1998 .