Evolutionary robotics

I l l u s t r a t I o n b y J u s t I n M e t z The auTomaTed design, construction, and deployment of autonomous and adaptive machines is an open problem. Industrial robots are an example of autonomous yet nonadaptive machines: they execute the same sequence of actions repeatedly. Conversely, unmanned drones are an example of adaptive yet non-autonomous machines: they exhibit the adaptive capabilities of their remote human operators. To date, the only force known to be capable of producing fully autonomous as well as adaptive machines is biological evolution. In the field of evolutionary robotics, one class of population-based metaheuristics—evolutionary algorithms—are used to optimize some or all aspects of an autonomous robot. The use of metaheuristics sets this subfield of robotics apart from the mainstream of robotics research, in which machine learning algorithms are used to optimize the control policy of a robot. As in other branches of computer science the use of a metaheuristic algorithm has a cost and a benefit. The cost is that it is not possible to guarantee if (or when) an optimal control policy will be found for a given robot. The benefit is few assumptions must be made

[1]  Craig W. Reynolds.  Flocks, herds, and schools: a distributed behavioral model , 1987, SIGGRAPH.

[2]  G. M. Werner.  Evolution of Communication in Artificial Organisms, Artifial Life II , 1991 .

[3]  Karl Sims,et al.  Evolving 3D Morphology and Behavior by Competition , 1994, Artificial Life.

[4]  Stefano Nolfi,et al.  Evolving mobile robots in simulated and real environments , 1995 .

[5]  Stefano Nolfi,et al.  Evolving Mobile Robots in Simulated and Real Environments , 1995, Artificial Life.

[6]  Inman Harvey,et al.  Noise and the Reality Gap: The Use of Simulation in Evolutionary Robotics , 1995, ECAL.

[7]  Lee Spector,et al.  Evolving teamwork and coordination with genetic programming , 1996 .

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

[9]  Jordan B. Pollack,et al.  Creating High-Level Components with a Generative Representation for Body-Brain Evolution , 2002, Artificial Life.

[10]  F. Iida,et al.  Iterative Product Engineering : Evolutionary Robot Design , 2002 .

[11]  Phil Husbands,et al.  Evolution of central pattern generators for bipedal walking in a real-time physics environment , 2002, IEEE Trans. Evol. Comput..

[12]  Lincoln Smith,et al.  Evolving controllers for a homogeneous system of physical robots: structured cooperation with minimal sensors , 2003, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[13]  Giulio Sandini,et al.  Developmental robotics: a survey , 2003, Connect. Sci..

[14]  Russ Tedrake,et al.  Efficient Bipedal Robots Based on Passive-Dynamic Walkers , 2005, Science.

[15]  Rolf Pfeifer,et al.  How the body shapes the way we think - a new view on intelligence , 2006 .

[16]  Chandana Paul,et al.  Morphological computation: A basis for the analysis of morphology and control requirements , 2006, Robotics Auton. Syst..

[17]  Hod Lipson,et al.  Resilient Machines Through Continuous Self-Modeling , 2006, Science.

[18]  Daniel Polani,et al.  How Information and Embodiment Shape Intelligent Information Processing , 2006, 50 Years of Artificial Intelligence.

[19]  Chandana Paul,et al.  Design and control of tensegrity robots for locomotion , 2006, IEEE Transactions on Robotics.

[20]  Gregory S. Chirikjian,et al.  Modular Self-Reconfigurable Robot Systems [Grand Challenges of Robotics] , 2007, IEEE Robotics & Automation Magazine.

[21]  Eduardo Izquierdo-Torres,et al.  Analysis of a Dynamical Recurrent Neural Network Evolved for Two Qualitatively Different Tasks: Walking and Chemotaxis , 2008, ALIFE.

[22]  Randall D. Beer,et al.  The Dynamics of Brain–Body–Environment Systems , 2008 .

[23]  Dario Floreano,et al.  Evolved swarming without positioning information: an application in aerial communication relay , 2009, Auton. Robots.

[24]  Soha Hassoun,et al.  Evolving soft robotic locomotion in PhysX , 2009, GECCO '09.

[25]  Charles Ofria,et al.  Evolving coordinated quadruped gaits with the HyperNEAT generative encoding , 2009, 2009 IEEE Congress on Evolutionary Computation.

[26]  L. Munari.  How the body shapes the way we think — a new view of intelligence , 2009 .

[27]  Anthony Kulis,et al.  Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies , 2009, Scalable Comput. Pract. Exp..

[28]  David Johan Christensen,et al.  Fractal Gene Regulatory Networks for Robust Locomotion Control of Modular Robots , 2010, SAB.

[29]  Stefano Nolfi,et al.  Active Categorical Perception of Object Shapes in a Simulated Anthropomorphic Robotic Arm , 2010, IEEE Transactions on Evolutionary Computation.

[30]  Randall D. Beer,et al.  Information Dynamics of Evolved Agents , 2010, SAB.

[31]  Josh Bongard,et al.  Morphological change in machines accelerates the evolution of robust behavior , 2011, Proceedings of the National Academy of Sciences.

[32]  Kenneth O. Stanley,et al.  Abandoning Objectives: Evolution Through the Search for Novelty Alone , 2011, Evolutionary Computation.

[33]  Arend Hintze,et al.  Integrated Information Increases with Fitness in the Evolution of Animats , 2011, PLoS Comput. Biol..

[34]  Fumiya Iida,et al.  Soft Robotics: Challenges and Perspectives , 2011, FET.

[35]  Yan Meng,et al.  Autonomous Self-Reconfiguration of Modular Robots by Evolving a Hierarchical Mechanochemical Model , 2011, IEEE Computational Intelligence Magazine.

[36]  D. Floreano,et al.  Historical contingency affects signaling strategies and competitive abilities in evolving populations of simulated robots , 2012, Proceedings of the National Academy of Sciences.

[37]  Joshua Evan Auerbach,et al.  On the relationship between environmental and morphological complexity in evolved robots , 2012, GECCO '12.

[38]  John Jan. Long,et al.  Darwin's Devices: What Evolving Robots Can Teach Us About the History of Life and the Future of Technology , 2012 .

[39]  Stéphane Doncieux,et al.  The Transferability Approach: Crossing the Reality Gap in Evolutionary Robotics , 2013, IEEE Transactions on Evolutionary Computation.

[40]  Hod Lipson,et al.  Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding , 2013, GECCO '13.