Evolutionary robotics


Evolutionary robotics is a field of research that employs evolutionary computation to generate robots that adapt to their environment through a process analogous to natural evolution. … Evolutionary robotics is typically applied to create control system for robots

How to evolve autonomous robots: Different approaches in evolutionary robotics
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Abstract number of parts or modules within the system; rather, it scales with the number of possible interactions between parts and modules. A methodology for evolving the control systems of autonomous robots has not yet been well established. In this paper we will show

Evolutionary robotics
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Evolutionary Robotics is a method for automatically generating artificial brains and morphologies of autonomous robots. This approach is useful both for investigating the design space of robotic applications and for testing scientific hypotheses of biological

Minimal simulations for evolutionary robotics
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For several years now, various researchers have endeavoured to apply artificial evolution to the automatic design of control systems for robots. One of the major challenges they face is how the fitness of evolving controllers should be tested when each evolutionary run typically

Evolutionary robotics and SAGA: the case for hill crawling and tournament selection
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This paper will look at an evolutionary approach to robotics partly at pragmatic issues, but primarily at theoretical issues associated with the evolutionary algorithms which are appropriate. Genetic Algorithms are not suitable in their usual form for the evolution of

Competitive co- evolutionary robotics : From theory to practice
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It is argued that competitive co-evolution is a viable methodology for developing truly autonomous and intelligent machines capable of setting their own goals in order to face new and continuously changing challenges. The paper starts giving an introduction to the

Embodied evolution: A response to challenges in evolutionary robotics
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We introduce Embodied Evolution (EE), a new methodology for conducting evolutionary robotics (ER). Embodied evolution uses a population of physical robots that evolve by reproducing with one another in the task environment. EE addresses several issues identi

Back to reality: Crossing the reality gap in evolutionary robotics
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In this work a new method to evolutionary robotics is proposed, it combines into a single framework, learning from reality and simulations. An illusory sub-system is incorporated as an integral part of an autonomous system. The adaptation of the illusory system results from

Behavior Chaining-Incremental Behavior Integration for Evolutionary Robotics .
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One of the open problems in autonomous robotics is how to consistently and scalably integrate new behaviors into a robot with an existing behavioral repertoire. In this work a new technique called behavior chaining is introduced, which allows for gradually expanding

Co- evolutionary design: Implications for evolutionary robotics
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Abstract Genetic Algorithms (GAs) typically work on static tness landscapes. In contrast, natural evolution works on tness landscapes that change over evolutionary time as a result of (amongst other things) co-evolution. The attractions of co- evolutionary design techniques

Evolutionary robotics at Sussex
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We give an overview of evolutionary robotics research at Sussex. We explain and justify our distinctive approaches to (artificial) evolution, and to the nature of robot control systems that are evolved. We illustrate by presenting results from research with evolved controllers for

Evolutionary robotics in artificial life and behavior engineering
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Since the first edition of the ER conference in 199 Evolutionary Robotics has become a well-established discipline with several groups worldwide continuously reporting new theoretical and practical advancements. Two different directions have emerged during these

Design strategies for evolutionary robotics
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Abstract: This paper deals with the question of how to balance evolutionary design and human expertise in order to best design robots which can learn speci c tasks. We study two behavioral tasks, approach and avoidance, and provide some preliminary results Keywords Genetic

Evolutionary mobile robotics
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Robotics represents an interesting application domain for evolutionary computation because robot parameter optimization often implies search of high-dimensional spaces characterized by nonlinear and non-differentiable relationships between variables, which make analytical

Darwin+ robots= evolutionary robotics : Challenges in automatic robot synthesis
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This paper reviews the use of artificial evolution as a means for automatic generation of locomotion controllers for physical robots and autonomous agents from the perspectives of evolutionary objectives and control architecture. An overview beginning with the pioneering

Evolutionary robotics : looking forward.
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Evolutionary robotics (Cliff et al. 1993; Nolfi and Floreano) is the study and application of autonomous robots developed through a self-organizational method based on artificial evolution. This approach stresses the importance of the study of systems that:(1) have a

Self-Assembly in Physical Autonomous Robots-the Evolutionary Robotics Approach.
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This research work illustrates the details of a methodological approach to the design of homogeneous neuro-controllers for self-assembly in physical autonomous robots in which no assumptions are made concerning how agents allocate roles. Artificial evolution is used

Punctuated anytime learning for evolutionary robotics
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Two main issues will be addressed in this paper: anytime learning without immediate feedback and dynamic simulator accuracy in evolutionary robotics . A solution for both of these issues is the use of a punctuated anytime learning system. Training with some form of

Reducing human design and increasing adaptivity in evolutionary robotics
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Evolutionary Robotics is a technique for automatic creation of autonomous robots that is inspired upon the Darwinian principle of selective reproduction of the fittest. It is a new approach on its own which looks at robots as autonomous artificial organisms that develop

An Evolutionary Robotics simulation of human minimal social interaction
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2 Results Our first experiments were not successful, the GA could not find a satisfactory solution to the task, it remained in a local minimum where agents stop on top of any object first encountered. This strategy is successful, if agents first encounter each other or if one


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