Evolutionary robotics: the Sussex approach

Harvey, Inman; Husbands, Phil; Cliff, Dave; Thompson, Adrian; Jakobi, Nick

doi:10.1016/s0921-8890(96)00067-x

Cited by 183 publications

(97 citation statements)

References 8 publications

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“…This research then falls under the rubric of the Evolution of Physical Systems (sometimes called embodied evolution), following in the footsteps of Harvey et al at Sussex (Harvey et al, 1997), Watson et al (Watson et al, 1999), and more recently Zykov (2004) and Yosinksi (2011).…”

Section: Tensegrity Robotsmentioning

confidence: 99%

Evolution of Locomotion on a Physical Tensegrity Robot

Khazanov¹,

Jocque²,

Rieffel³

2014

Artificial Life 14: Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems

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Due to their high strength-to-weight ratio, robustness and deformability, tensegrity structures are an appealing platform for the emerging field of soft robotics, with applications ranging from search-and-rescue to field-deployable structures. Unfortunately, these properties also make tensegrities challenging to control through conventional means. In this paper we describe a novel means of vibration-based tensegrity actuation which allows for the manual control of a physical tensegrity robot in the plane as well as state-machine based target following. We demonstrate the evolution of effective gaits using only physical evaluations of the robot, and further demonstrate how a combination of the state-machine with the hill climber allows for the hands-off automation of the evolutionary process. We conclude with a description of how this can lead to a bootstrapping effect, with the potential to significantly accelerate and automate the physical evolution of our tensegrity robot.

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Section: Tensegrity Robotsmentioning

confidence: 99%

Evolution of Locomotion on a Physical Tensegrity Robot

Khazanov¹,

Jocque²,

Rieffel³

2014

Artificial Life 14: Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems

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“…Brooks (1992) proposed the idea of evolving primitives of a behavior language for autonomous mobile robots, e ectively combining a subsumption architecture (Brooks, 1986) with a genetic programming approach (Koza, 1992). The term Evolutionary Robotics has been coined by a group of researchers at the University of Sussex (Cli , Harvey, & Husbands, 1993) whose approach is based on a combination of simulations and physical robots guided by e v olutionary Dynamical-Recurrent-Neural-Networks (Harvey, Husbands, Cli , Thompson, & Jakobi, 1997). The Sussex group has developed a new evolutionary paradigm called Species Adaptation Genetic Algorithm (Harvey, 1992(Harvey, , 1993 for incrementally evolving neurocontrollers and patterns of logical gates and connections for recon gurable circuits (Thompson, Harvey, & Husbands, 1996).…”

Section: Related Workmentioning

confidence: 99%

Evolutionary neurocontrollers for autonomous mobile robots

1998

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In this article we describe a methodology for evolving neurocontrollers of autonomous mobile robots without human intervention. The presentation, which spans from technological and methodological issues to several experimental results on evolution of physical mobile robots, covers both previous and recent w ork in the attempt to provide a uni ed picture within which the reader can compare the e ects of systematic variations on the experimental settings. After describing some key principles for building mobile robots and tools suitable for experiments in adaptive robotics, we g i v e a n o verview of di erent approaches to evolutionary robotics and present our methodology. We start reviewing two basic experiments showing that di erent e n vironments can shape very di erent behaviors and neural mechanisms under very similar selection criteria. We then address the issue of incremental evolution in two di erent experiments from the perspective of changing environments and robot morphologies. Finally, w e i n vestigate the possibility o f e v olving plastic neurocontrollers and analyze an evolved neurocontroller that relies on fast and continuously changes synapses characterized by dynamic stability. We conclude by reviewing the implications of this methodology for engineering, biology, cognitive science, and arti cial life, and point at future directions of research.

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“…Evolutionary robotics (ER) [1,2], the application of evolutionary algorithms (EAs) to robot design, has shown itself to be a powerful technique. The ability of EAs to find novel solutions in a large or unfamiliar search space has been demonstrated, e.g., with Sims' swimming robots [3] and in antenna design [4].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Sampling Strategies for Parameter Estimation of a Robot Simulator

Klaus

Glette

Tørresen

2012

Simulation, Modeling, and Programming for Autonomous Robots

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Abstract. Methods for dealing with the problem of the "reality gap" in evolutionary robotics are described. The focus is on simulator tuning, in which simulator parameters are adjusted in order to more accurately model reality. We investigate sample selection, which is the method of choosing the robot controllers, evaluated in reality, that guide simulator tuning. Six strategies for sample selection are compared on a robot locomotion task. It is found that strategies that select samples that show high fitness in simulation greatly outperform those that do not. One such strategy, which selects the sample that is the expected fittest as well as the most informative (in the sense of producing the most disagreement between potential simulators), results in the creation of a nearly optimal simulator in the first iteration of the simulator tuning algorithm.

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Evolutionary robotics: the Sussex approach

Cited by 183 publications

References 8 publications

Evolution of Locomotion on a Physical Tensegrity Robot

Evolution of Locomotion on a Physical Tensegrity Robot

Evolutionary neurocontrollers for autonomous mobile robots

A Comparison of Sampling Strategies for Parameter Estimation of a Robot Simulator

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