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15th Genetic and evolutionary Computation Conference (GECCO)

作者： Samuelsen, Eivind Glette, Kyrre Torresen, Jim Univ Oslo Inst Informat N-0316 Oslo Norway

ISBN: (纸本)9781450319638

This paperproposes anapproach to representing robot morphology and control, using a two-level description linked to two different physical axes of development. The bioinspired encoding produces robots with animal-like bilateral limbed morphology with co-evolved control parameters using a central pattern generator-based modular artificial neural network. Experiments are performed on optimizing a simple simulated locomotion problem, using multi-objective evolution with two secondary objectives. The results show that the representation is capable of producing a variety of viable designs even with a relatively restricted set of parameters and a very simple control system. Furthermore, the utility of a cumulative encoding over a non-cumulative approach is demonstrated. We also show that the representation is viable for real-life reproduction by automatically generating CAD files, 3D printing the limbs, and attaching off-the-shelf servomotors.

关键词： evolutionary robotics morphological evolution embryogenic encoding

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The Dynamically Extended Mind A Minimal Modeling Case Study

The Dynamically Extended Mind A Minimal Modeling Case Study

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IEEE Congress on evolutionary Computation

作者： Froese, Tom Gershenson, Carlos Rosenblueth, David A. Univ Nacl Autonoma Mexico IIMAS Dept Ciencias Comp Mexico City 01000 DF Mexico

ISBN: (纸本)9781479904549;9781479904532

The extended mind hypothesis has stimulated much interest in cognitive science. However, its core claim, i.e. that the process of cognition can extend beyond the brain via the body and into the environment, has been heavily criticized. A prominent critique of this claim holds that when some part of the world is coupled to a cognitive system this does not necessarily entail that the part is also constitutive of that cognitive system. This critique is known as the "coupling-constitution fallacy". In this paper we respond to this reductionist challenge by using an evolutionary robotics approach to create a minimal model of two acoustically coupled agents. We demonstrate how the interaction process as a whole has properties that cannot be reduced to the contributions of the isolated agents. We also show that the neural dynamics of the coupled agents has formal properties that are inherently impossible for those neural networks in isolation. By keeping the complexity of the model to an absolute minimum, we are able to illustrate how the coupling-constitution fallacy is in fact based on an inadequate understanding of the constitutive role of nonlinear interactions in dynamical systems theory.

关键词： philosophy of mind cognitive science dynamical approach extended mind hypothesis evolutionary robotics

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Evolved Center-crossing Recurrent Synaptic Delay Based Neural Networks for Biped Locomotion Control

Evolved Center-crossing Recurrent Synaptic Delay Based Neura...

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IEEE Congress on evolutionary Computation

作者： Santos, Jose Univ A Coruna Dept Comp Sci La Coruna 15071 Spain

ISBN: (纸本)9781479904549;9781479904532

This paper combines the center-crossing condition in artificial neural networks that incorporate synaptic delays in their connections and which act as Central Pattern Generators (CPGs) for biped controllers. Recurrent synaptic delay based neural networks allow greater time reasoning capabilities in the neural controllers, outperforming the results of continuous time recurrent neural networks, the neural model most used as CPG for biped robot locomotion related behaviors. Simulated evolution is used to automatically obtain neural controllers for walking behaviors, showing the capabilities of the synaptic delay based neural networks for the temporal coordination of the biped joints in difficult surfaces.

关键词： Synaptic delay based neural networks continuous time recurrent ANNs center-crossing ANNs evolutionary robotics

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GESwarm: Grammatical Evolution for the Automatic Synthesis of Collective Behaviors in Swarm robotics 13

GESwarm: Grammatical Evolution for the Automatic Synthesis o...

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15th Genetic and evolutionary Computation Conference (GECCO)

作者： Ferrante, Eliseo Duenez-Guzman, Edgar Turgut, Ali Emre Wenseleers, Tom Socioecol & Social Evolut KULeuven 59 Naamsestr Bus 2466 B-3000 Louvain Belgium THK Univ Mech Dept Ankara 06790 Turkey

ISBN: (纸本)9781450319638

In this paper we propose GESwarm, a novel tool that can automatically synthesize collective behaviors for swarms of autonomous robots through evolutionary robotics. evolutionary robotics typically relies on artificial evolution for tuning the weights of an artificial neural network that is then used as individual behavior representation. The main caveat of neural networks is that they are very difficult to reverse engineer, meaning that once a suitable solution is found, it is very difficult to analyze, to modify, and to tease apart the inherent principles that lead to the desired collective behavior. In contrast, our representation is based on completely readable and analyzable individual-level rules that lead to a desired collective behavior. The core of our method is a grammar that can generate a rich variety of collective behaviors. We test GESwarm by evolving a foraging strategy using a realistic swarm robotics simulator. We then systematically compare the evolved collective behavior against an hand-coded one for performance, scalability and flexibility, showing that collective behaviors evolved with GESwarm can outperform the hand-coded one.

关键词： swarm robotics evolutionary robotics genetic programming

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Evolution of Neural Controllers for Simulated and Real Quadruped Robots

Evolution of Neural Controllers for Simulated and Real Quadr...

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2nd International Conference on Robot, Vision and Signal Processing (RVSP)

作者： Farooq, Sehar Shahzad Kim, KyungJoong Sejong Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9781479931842

evolutionary robotics is an approach that employs evolutionary computation to develop a controller for an autonomous robotic system. evolutionary computing usually operates depending on a population of candidate controllers, initially selected from a random distribution. The population is iteratively modified according to the fitness function. In this paper, an automatic control system is designed for quadruped robots using an evolutionary Neural Network (ENN) and the performance is measured in terms of the distance travelled by the robot from its origin. The evolved neural controllers are analyzed in the simulation environment and the results are implemented in a real quadruped robot. The comparison between the simulated and real robot shows the performance of the quadruped robot in terms of number of iterations over the distance covered in the desired direction. The developed ENN helps the robot to choose the best possible solution to achieve the maximum distance.

关键词： component evolutionary robotics Quadruped Robot evolutionary Neural Controller Systematic Analysis

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Behavioral Repertoire Learning in robotics 13

Behavioral Repertoire Learning in Robotics

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15th Genetic and evolutionary Computation Conference (GECCO)

作者： Cully, Antoine Mouret, Jean-Baptiste Univ Paris 06 ISIR CNRS UMR 7222 F-75252 Paris 05 France

ISBN: (纸本)9781450319638

Learning in robotics typically involves choosing a simple goal (e.g. walking) and assessing the performance of each controller with regard to this task (e.g. walking speed). However, learning advanced, input-driven controllers (e.g. walking in each direction) requires testing each controller on a large sample of the possible input signals. This costly process makes difficult to learn useful low-level controllers in robotics. Here we introduce BR-Evolution, a new evolutionary learning technique that generates a behavioral repertoire by taking advantage of the candidate solutions that are usually discarded. Instead of evolving a single, general controller, BR-evolution thus evolves a collection of simple controllers, one for each variant of the target behavior;to distinguish similar controllers, it uses a performance objective that allows it to produce a collection of diverse but high-performing behaviors. We evaluated this new technique by evolving gait controllers for a simulated hexapod robot. Results show that a single run of the EA quickly finds a collection of controllers that allows the robot to reach each point of the reachable space. Overall, BR-Evolution opens a new kind of learning algorithm that simultaneously optimizes all the achievable behaviors of a robot.

关键词： evolutionary Algorithm evolutionary robotics Mobile robotics Behavior Exploration Novelty Search

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Self-adapting Fitness Evaluation Times for On-line Evolution of Simulated Robots 13

Self-adapting Fitness Evaluation Times for On-line Evolution...

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15th Genetic and evolutionary Computation Conference (GECCO)

作者： Dinu, Cristian M. Dimitrov, Plamen Weel, Berend Eiben, A. E. Vrije Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9781450319638

This paper is concerned with on-line evolutionary robotics, where robot controllers are being evolved during a robots' operative time. This approach offers the ability to cope with environmental changes without human intervention, but to be effective it needs an automatic parameter control mechanism to adjust the evolutionary algorithm (EA) appropriately. In particular, mutation step sizes (sigma) and the time spent on fitness evaluation (tau) have a strong influence on the performance of an EA. In this paper, we introduce and experimentally validate a novel method for self-adapting tau during runtime. The results show that this mechanism is viable: the EA using this self-adaptative control scheme consistently shows decent performance without a priori tuning or human intervention during a run.

关键词： evolutionary robotics Swarm robotics On-line Evolution Embodied Evolution Dynamic Environments

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An approach to the synthesis of humanoid robot dance using non-interactive evolutionary techniques.

An approach to the synthesis of humanoid robot dance using n...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Malachy Eaton Dept. Computer Science and Information Systems University of Limerick Limerick Ireland

ISBN: (纸本)9781479906505

After bipedal locomotion, dance is one of the most commonly studied behaviours for researchers seeking to replicate human-like motion in humanoid robots. Many of the methods employed involve direct interaction with, or imitation of, human participant(s). For example, the generation of dance movements using interactive evolutionary computation (IEC) involves the replacement of an objective fitness function with the subjective evaluations of human observer(s). In this paper we present an alternative approach to the synthesis of humanoid robot dance using non-interactive evolutionary computation (non-IEC) methods. We propose a novel fitness function for the evolution of robotic dance, and we present initial results of the application of this evolutionary process to the generation of dance patterns for the 18-DOF Bioloid humanoid robot. We conclude that even without the presence of a human or humans in the evolutionary loop, it is possible to produce surprisingly lifelike and novel dances using this approach.

关键词： genetic algorithms humanoid robots evolutionary robotics dance behaviours Humanoid robots Dancing Genetic Algorithm (GA) Human non-interactive synthesis evolutionary

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Cooperative Transport by a Swarm Robotic System Based on CMA-NeuroES Approach

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2013年第6期17卷 932-942页

作者： Yu, Tian Yasuda, Toshiyuki Ohkura, Kazuhiro Matsumura, Yoshiyuki Goka, Masanori Hiroshima Univ Grad Sch Engn 1-4-1 Kagamiyama Higashihiroshima Hiroshima 7398527 Japan Shinshu Univ Grad Sch Sci & Technol Ueda Nagano 3868567 Japan Fukuyama Univ Grad Sch Engn Hiroshima 7290251 Japan

Swarm robotic systems consist of many homogeneous autonomous robots with no type of global controllers. It is difficult to design controllers for such behavioral systems, since the behavior of system level is the emergent results of the dynamical interaction between the systems and the environment. This paper uses a method that in which robot controllers are designed by a covariance matrix adaptation evolution strategy (CMA-ES) with artificial neural networks. This approach is referred to as CMA-NeuroES. Among the many evolutionary algorithms for evolving artificial neural networks, two conventionally representation approaches, fast evolution strategies and differential evolution, are used for comparison. The cooperative transport problem is used as a benchmark of swarm robotic systems to test their performance. Results show that CMA-NeuroES has the overall best performance of the three.

关键词： swarm robotic systems evolving artificial neural network evolutionary robotics covariance matrix adaptation evolution strategy

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Generating Cooperative Collective Behavior in Swarm Robotic Systems

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2013年第5期17卷 699-706页

作者： Ohkura, Kazuhiro Yasuda, Toshiyuki Matsumura, Yoshiyuki Hiroshima Univ Grad Sch Engn 1-4-1 Kagamiyama Higashihiroshima 7398527 Japan Shinshu Univ Fac Text Sci & Technol Ueda Nagano 3868567 Japan

Swarm robotics research involves multirobot systems that consist of many homogeneous autonomous robots but no global controller. In this paper, an evolutionary robotics approach using an artificial neural network is applied to a swarm robotic system. Conventionally, the neural network evolved using only synaptic weights under the condition of a fixed topology. Our research group has been developing a novel approach to a topology and weight evolving artificial neural network named Mutation-Based Evolving Artificial Neural Network (MBEANN). A series of computer simulations shows that MBEANN yields better results in terms of flexibility than conventional solutions to the cooperative package-pushing problem.

关键词： swarm robotics evolutionary robotics topology and weight evolving artificial neural network (TWEANN) MBEANN

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