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248
Challenges in Evolving Controllers for Physical Robots
, 1996
"... This paper discusses the feasibility of applying evolutionary methods to automatically generating controllers for physical mobile robots. We overview the state of the art in the field, describe some of the main approaches, discuss the key challenges, unanswered problems, and some promising direction ..."
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Cited by 156 (6 self)
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This paper discusses the feasibility of applying evolutionary methods to automatically generating controllers for physical mobile robots. We overview the state of the art in the field, describe some of the main approaches, discuss the key challenges, unanswered problems, and some promising directions. 1 Introduction This paper is concerned with the distant goal of automated synthesis of robot controllers. Specifically, we focus on the problems of evolving controllers for physically embodied and embedded systems that deal with all of the noise and uncertainly present in the world. We will also address some systems that evolve both the morphology and the controller of a robot. Within the scope of this paper we define morphology as the physical, embodied characteristics of the robot, such as its mechanics and sensor organization. Given that definition, the only examples of evolving both morphology and control exist in simulation. Evolutionary methods for automated hardware design are an ...
Evolutionary robots with on-line self-organization and behavioral fitness
- Neural Networks
, 2000
"... We address two issues in Evolutionary Robotics, namely the genetic encoding and the performance criterion, also known as fitness function. For the first aspect, we suggest to encode mechanisms for parameter self-organization, instead of the parameters themselves as in conventional approaches. We arg ..."
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Cited by 104 (7 self)
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We address two issues in Evolutionary Robotics, namely the genetic encoding and the performance criterion, also known as fitness function. For the first aspect, we suggest to encode mechanisms for parameter self-organization, instead of the parameters themselves as in conventional approaches. We argue that the suggested encoding generates systems that can solve more complex tasks and are more robust to unpredictable sources of change. We support our arguments with a set of experiments on evolutionary neural controller for physical robots and compare them to conventional encoding. In addition, we show that when also the genetic encoding is left free to evolve, artificial evolution will select to exploit mechanisms of self-organization. For the second aspect, we shall discuss the role of the performance criterion, also known as fitness function, and suggest Fitness Space as a framework to conceive fitness functions in Evolutionary Robotics. Fitness Space can be used as a guide to design fitness functions as well as to compare different experiments in Evolutionary Robotics. 1 1
Evolutionary neurocontrollers for autonomous mobile robots
- NEURAL NETWORKS
, 1998
"... 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 ..."
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Cited by 98 (10 self)
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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 work in the attempt to provide a uni ed picture within which the reader can compare the effects 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 give an overview of different approaches to evolutionary robotics and present our methodology. We start reviewing two basic experiments showing that different environments can shape very different behaviors and neural mechanisms under very similar selection criteria. We then address the issue of incremental evolution in two different experiments from the perspective of changing environments and robot morphologies. Finally, we investigate the possibility of evolving 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 artificial life, and point at future directions of research.
Evolution of plastic neurocontrollers for situated agents. Animals to Animats
, 1989
"... In this paper we investigate a novel approach to the evolutionary development of autonomous situated agents based on the assumption that the neural mechanisms underlying ontogenetic learning are themselves developed and shaped by the evolutionary process. A genetic algorithm is used to evolve neural ..."
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Cited by 91 (16 self)
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In this paper we investigate a novel approach to the evolutionary development of autonomous situated agents based on the assumption that the neural mechanisms underlying ontogenetic learning are themselves developed and shaped by the evolutionary process. A genetic algorithm is used to evolve neural structures that can be continuously modi ed during life according to the mechanisms speci ed in the genotype. The evolutionary process is carried out on a real mobile robot. The analysis of one of the best evolved individuals shows rapid development of stable behavior mediated by fast-changing synapses which are dynamically stable. 1
An Indexed Bibliography of Genetic Algorithms in Power Engineering
, 1995
"... s: Jan. 1992 -- Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993 -- Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1 -- Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991 -- Dec. 1994 ffl P: Index to Scientific & Technical Proceed ..."
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Cited by 90 (10 self)
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s: Jan. 1992 -- Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993 -- Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1 -- Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991 -- Dec. 1994 ffl P: Index to Scientific & Technical Proceedings: Jan. 1986 -- Feb. 1995 (except Nov. 1994) ffl EI A: The Engineering Index Annual: 1987 -- 1992 ffl EI M: The Engineering Index Monthly: Jan. 1993 -- Dec. 1994 The following GA researchers have already kindly supplied their complete autobibliographies and/or proofread references to their papers: Dan Adler, Patrick Argos, Jarmo T. Alander, James E. Baker, Wolfgang Banzhaf, Ralf Bruns, I. L. Bukatova, Thomas Back, Yuval Davidor, Dipankar Dasgupta, Marco Dorigo, Bogdan Filipic, Terence C. Fogarty, David B. Fogel, Toshio Fukuda, Hugo de Garis, Robert C. Glen, David E. Goldberg, Martina Gorges-Schleuter, Jeffrey Horn, Aristides T. Hatjimihail, Mark J. Jakiela, Richard S. Judson, Akihiko Konaga...
Embodied Evolution: Distributing an Evolutionary Algorithm in a Population of Robots
- Robotics and Autonomous Systems
, 2002
"... We introduce Embodied Evolution (EE) as a new methodology for evolutionary robotics (ER). EE uses a population of physical robots that autonomously reproduce with one another while situated in their task environment. This constitutes a fully distributed evolutionary algorithm embodied in physical ro ..."
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Cited by 81 (0 self)
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We introduce Embodied Evolution (EE) as a new methodology for evolutionary robotics (ER). EE uses a population of physical robots that autonomously reproduce with one another while situated in their task environment. This constitutes a fully distributed evolutionary algorithm embodied in physical robots. Several issues identified by researchers in the evolutionary robotics community as problematic for the development of ER are alleviated by the use of a large number of robots being evaluated in parallel. Particularly, EE avoids the pitfalls of the simulate-and-transfer method and allows the speed-up of evaluation time by utilizing parallelism. The more novel features of EE are that the evolutionary algorithm is entirely decentralized, which makes it inherently scalable to large numbers of robots, and that it uses many robots in a shared task environment, which makes it an interesting platform for future work in collective robotics and Artificial Life. We have built a population of eight robots and successfully implemented the first example of Embodied Evolution by designing a fully decentralized, asynchronous evolutionary algorithm. Controllers evolved by EE outperform a hand-designed controller in a simple application. We introduce our approach and its motivations, detail our implementation and initial results, and discuss the advantages and limitations of EE. © 2002 Elsevier Science B.V. All rights reserved.
Evolving non-Trivial Behaviors on Real Robots: an Autonomous Robot that Picks up Objects
- ROBOTICS AND AUTONOMOUS SYSTEMS
, 1995
"... Recently, a new approach that involves a form of simulated evolution has been proposed for the building of autonomous robots. However, it is still not clear if this approach may be adequate to face real life problems. In this paper we show how control systems that perform a non-trivial sequence of b ..."
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Cited by 76 (15 self)
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Recently, a new approach that involves a form of simulated evolution has been proposed for the building of autonomous robots. However, it is still not clear if this approach may be adequate to face real life problems. In this paper we show how control systems that perform a non-trivial sequence of behaviors can be obtained with this methodology by carefully designing the conditions in which the evolutionary process operates. In the experiment described in the paper, a mobile robot is trained to locate, recognize, and grasp a target object. The controller of the robot has been evolved in simulation and then downloaded and tested on the real robot.
Evolving Robust Gaits with AIBO
, 2000
"... An evolutionary algorithm is used to evolve gaits with the Sony entertainment robot, AIBO. All processing is handled by the robot's on-board computer with individuals evaluated using the robot's hardware. By sculpting the experimental environment, we increase robustness to different surfac ..."
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Cited by 68 (1 self)
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An evolutionary algorithm is used to evolve gaits with the Sony entertainment robot, AIBO. All processing is handled by the robot's on-board computer with individuals evaluated using the robot's hardware. By sculpting the experimental environment, we increase robustness to different surface types and different AIBOs. Evolved gaits are faster than those created by hand. Using this technique we evolve a gait used in the consumer version of AIBO.
DRAMA, a Connectionist Architecture for Control and Learning in Autonomous Robots
- Adaptive Behavior
, 1999
"... this paper gives ..."
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