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Genetic and evolutionary Computation Conference (GECCO)

作者： Munk, Rasmus Hart, Emma Paechter, Ben Edinburgh Napier Univ Sch Comp Edinburgh Midlothian Scotland

ISBN: (纸本)9781450349390

Recent research in small populations of Thymio II robots illustrated the relative benefits of populations distinguishing heritable and learning features in robots for a simple obstacle avoidance task. Here we scientifically validate these results by repeating them using a simulation. An additional benefit of this work is to provide confidence in the simulation model. This is important because evolutionary swarm robotics experiments can be very time consuming to run in real robots. Having a reliable simulation allows many more experiments to be run in simulation with only the most interesting results needing to be verified with real robots. We describe the development of a simulation using RoboRobo that's using the same three-tier learning framework that was demonstrated in the real-world. The simulation is shown to replicate the real-world results in terms of illustrating the relative benefits of each type of learning, and if anything, indicates that social learning can be more powerful than originally thought.

关键词： evolutionary robotics Simulated robotics Social Learning

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Effect of Animat Complexity on the Evolution of Hierarchical Control 17

Effect of Animat Complexity on the Evolution of Hierarchical...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Moore, Jared M. Clark, Anthony J. McKinley, Philip K. Grand Valley State Univ Sch Comp & Informat Syst Allendale MI 49401 USA Missouri State Univ Comp Sci Dept Springfield MO USA Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA

ISBN: (纸本)9781450349208

Animal movements are realized by a combination of high-level control from the nervous system and joint-level movement provided by the musculoskeletal system. The digital muscle model (DMM) emulates the low-level musculoskeletal system and can be combined with a high-level artificial neural network (ANN) controller forming a hybrid control strategy. Previous work has shown that, compared to ANN-only controllers, hybrid ANN/DMM controllers exhibit similar performance with fewer synapses, suggesting that some computation is offloaded to the low-level DMM. An open question is how the complexity of the robot, in terms of the number of joints, affects the evolution of the ANN control structure. We explore this question by evolving both hybrid controllers and ANN-only controllers for worm-like animats of varying complexity. Specifically, the number of joints in the worms ranges from 1 to 12. Consistent with an earlier study, the results demonstrate that, in most cases, hybrid ANN/DMM controllers exhibit equal or better performance than ANN-only controllers. In addition, above a threshold for animat complexity (number of joints), the ANNs for one variant of the hybrid controllers have significantly fewer connections than the ANN-only controllers.

关键词： evolutionary robotics artificial neural networks digital muscle model animats controller evolution

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Novel Approaches to Cooperative Coevolution of Heterogeneous Multiagent Systems

Novel Approaches to Cooperative Coevolution of Heterogeneous...

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作者： Jorge Miguel Carvalho Gomes Universidade de Lisboa

学位级别：硕士

Heterogeneous multirobot systems are characterised by the morphological and/or behavioural heterogeneity of their constituent robots. These systems have a number of advantages over the more common homogeneous multirobot systems: they can leverage specialisation for increased efficiency, and they can solve tasks that are beyond the reach of any single type of robot, by combining the capabilities of different robots. Manually designing control for heterogeneous systems is a challenging endeav- our, since the desired system behaviour has to be decomposed into behavioural rules for the individual robots, in such a way that the team as a whole cooperates and takes advantage of specialisation. evolutionary robotics is a promising alternative that can be used to automate the synthesis of controllers for multirobot systems, but so far, research in the field has been mostly focused on homogeneous systems, such as swarm robotics systems. Cooperative coevolutionary algorithms (CCEAs) are a type of evolutionary algorithm that facilitate the evolution of control for heteroge- neous systems, by working over a decomposition of the problem. In a typical CCEA application, each agent evolves in a separate population, with the evaluation of each agent depending on the cooperation with agents from the other coevolving popu- lations. A CCEA is thus capable of projecting the large search space into multiple smaller, and more manageable, search spaces. Unfortunately, the use of coopera- tive coevolutionary algorithms is associated with a number of challenges. Previous works have shown that CCEAs are not necessarily attracted to the global optimum, but often converge to mediocre stable states; they can be inefficient when applied to large teams; and they have not yet been demonstrated in real robotic systems, nor in morphologically heterogeneous multirobot systems. In this thesis, we propose novel methods for overcoming the fundamental chal- lenges in cooperative coevolutionary algorithms

关键词： evolutionary robotics cooperative coevolutionary algorithms heterogeneous multirobot systems

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Exploiting Environmental Differentiation to Promote Evolvability in Artificial Evolution 17

Exploiting Environmental Differentiation to Promote Evolvabi...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Carvalho, Jonata Tyska Nolfi, Stefano Fed Univ Rio Grande FURG Ctr Computat Sci C3 Av ItaliaKm 8 BR-96203900 Rio Grande Brazil Natl Res Council CNR Inst Cognit Sci & Technol ISTC Via S Martino Battaglia 44 I-00185 Rome Italy

ISBN: (纸本)9781450349390

In this work we investigate the possibility to exploit environmental differentiation to promote evolvability in artificial evolution. More specifically we propose a new algorithm and demonstrate how agents evolved for the ability to solve the double-pole balancing problem in differentiated environmental conditions among the population outperform agents evolved in homogeneous environmental conditions. The algorithm operates by evolving the agents on multiple environmental niches with randomly varying environmental characteristics and by enabling agents displaying superior performance in other niches to colonize them. Agents evolved through the proposed algorithm outperform agents evolved in homogeneous environments, either on stable or temporally varying environments.

关键词： evolutionary robotics Genetic Algorithms Evolvability Niches

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Autonomous Evolution of Sensory and Actuator Driver Layers Through Environmental Constraints

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2017年

作者： taehoon anthony choi

Autonomous Evolution of Sensory and Actuator Driver Layers Through Environmental Constraints by Choi, Taehoon Anthony; Choi, Taehoon Anthony; published by

关键词： actuator autonomous autonomous robots constraints driver environmental evolutionary robotics mobile robots

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Is Social Learning More Than Parameter Tuning? 17

Is Social Learning More Than Parameter Tuning?

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Genetic and evolutionary Computation Conference (GECCO)

作者： Heinerman, Jacqueline Stork, Jorg Coy, Margarita Alejandra Rebolledo Hubert, Julien Eiben, A. E. Bartz-Beielstein, Thomas Haasdijk, Evert Vrije Univ Amsterdam Amsterdam Netherlands TH Koln Cologne Germany

ISBN: (纸本)9781450349390

Social learning enables multiple robots to share learned experiences while completing a task. The literature offers examples where robots trained with social learning reach a higher performance compared to their individual learning counterparts [e.g, 2, 4]. No explanation has been advanced for that observation. In this research, we present experimental results suggesting that a lack of tuning of the parameters in social learning experiments could be the cause. In other words: the better the parameter settings are tuned, the less social learning can improve the system performance.

关键词： evolutionary robotics social learning neural networks parameter tuning

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Evolution and Analysis of Embodied Spiking Neural Networks Reveals Task-Specific Clusters of Effective Networks 17

Evolution and Analysis of Embodied Spiking Neural Networks R...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Vasu, Madhavun Candadai Izquierdo, Eduardo J. Indiana Univ Bloomington IN 47405 USA

ISBN: (纸本)9781450349208

Elucidating principles that underlie computation in neural networks is currently a major research topic of interest in neuroscience. Transfer Entropy (TE) is increasingly used as a tool to bridge the gap between network structure, function, and behavior in fMRI studies. Computational models allow us to bridge the gap even further by directly associating individual neuron activity with behavior. However, most computational models that have analyzed embodied behaviors have employed non-spiking neurons. On the other hand, computational models that employ spiking neural networks tend to be restricted to disembodied tasks. We show for the first time the artificial evolution and TE-analysis of embodied spiking neural networks to perform a cognitively-interesting behavior. Specifically, we evolved an agent controlled by an Izhikevich neural network to perform a visual categorization task. The smallest networks capable of performing the task were found by repeating evolutionary runs with different network sizes. Informational analysis of the best solution revealed task-specific TE-network clusters, suggesting that within-task homogeneity and across-task heterogeneity were key to behavioral success. Moreover, analysis of the ensemble of solutions revealed that task-specificity of TE-network clusters correlated with fitness. This provides an empirically testable hypothesis that links network structure to behavior.

关键词： Spiking Neural networks evolutionary algorithms Transfer Entropy Information Theory evolutionary robotics

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Benefits of Lamarckian Evolution for Morphologically Evolving Robots 17

Benefits of Lamarckian Evolution for Morphologically Evolvin...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Jelisavcic, Milan Kiesel, Rafael Glette, Kyrre Haasdijk, Evert Eiben, A. E. Vrije Univ Amsterdam Amsterdam Netherlands Univ Oslo Oslo Norway

ISBN: (纸本)9781450349390

Implementing lifetime learning by means of on-line evolution, we establish an indirect encoding scheme that combines Compositional Pattern Producing Networks (CPPNs) and Central Pattern Generators (CPGs) as a relevant learner and controller for open-loop gait controllers in modular robots which have evolving morphologies. Experimental validation on the morphologically evolved robots shows that a Lamarckian setup with CPPN-CPG provides substantial benefits compared to controllers learned from scratch.

关键词： evolutionary robotics On-line evolution Indirect encoding Lamarckian evolution Gait learning

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Study on a Pneumatically Actuated Robot for Simulating evolutionary Developmental Process of Musculoskeletal Structures

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JOURNAL OF robotics AND MECHATRONICS 2016年第2期28卷 226-233页

作者： Ryu, Hideyuki Nakata, Yoshihiro Okadome, Yuya Nakamura, Yutaka Ishiguro, Hiroshi Osaka Univ Grad Sch Engn Sci Dept Syst Innovat 1-3 Machikaneyama Cho Toyonaka Osaka 5608531 Japan

Under the effects of surroundings such as gravitational force, ambient temperature, and chemical substances, each animal has acquired an optimized body structure through its evolution. For example, vertebrate land animals have a sophisticated musculoskeletal structure including not only monoarticular muscles but also multiarticular muscles to support their weight against gravitational force. Many researchers have developed musculoskeletal robots with a biarticular muscle mechanism that enables them to execute physical tasks similar to the mimicked animal. However, the developmental process of the musculoskeletal structure has not been examined in detail in past studies. In this study, we developed a musculoskeletal robot with redundant air cylinders to investigate the developmental process of the body structure of the animal. We proposed a switching mechanism between several muscle structures called the actuator network system (ANS). In the ANS, the selection of mutually interconnected, simultaneously activated air cylinders is changed by switching the interconnections. The experimental results indicate that by changing the connection of the cylinders and the inner pressure of the connected cylinders, i.e., the strength of the connection, the response of the robot to external forces can be modified, thus demonstrating the feasibility of our approach.

关键词： actuator network system evolutionary robotics multiarticular muscle musculoskeletal robot

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Automated Design of Embodied Machines: Optimization Algorithms for Soft Robot Morphologies and Behaviors

Automated Design of Embodied Machines: Optimization Algorith...

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作者： Cheney Nicholas Arthur Cornell University

学位级别：Ph . D

The current state of robotics relies largely on hand designed morphologies and controllers. This paradigm of robotics is well suited for controlled and static environments like warehouses or factory floors, but this type of robot often fails to extrapolate to autonomous behaviors in unpredictable and dynamic environments. In contrast, biological animals have evolved to seamlessly interact with the uncertainty of the real world. They accomplish this feat, in part, through specialized and complex morphologies that employ compliant materials. In this work, I explore the interactions of autonomous embodied agents’ brains and bodies with each other, and with the outside environment, through the evolution of soft robot morphologies and controllers. These interactions are first explored by evolving robots that perform complex and effective behaviors without high-level controllers in order to demonstrate the potential of morphological computation in compliant bodies. The study of morphological computation is further explored by also demonstrating effective behavior in tasks which are unapproachable with traditional rigid body robots (like squeezing and folding oneself). The focus on morphologically-driven behaviors is extended by evolving soft robots with neural-esque spiking muscles and demonstrating the optimization of physically embodied information pathways, exemplify the continuum between morphologies and controllers in embodied systems. I then turn to the simultaneous optimization of complex morphologies and high-level controllers, using the theory of embodied cognition to hypothesize that the specialization of morphologies and controllers to one another has been hindering the evolution of complex embodied machines. Results here demonstrate that a proposed algorithm for “morphological innovation protection”, which temporarily reduces selection pressure on newly mutated morphologies to enable readaptation of the coupled brain-body systems, produces significantly more f

关键词： Communication and the arts Applied sciences Psychology 3d printing Design automation Embodied cognition evolutionary robotics Morphological computation Soft robotics

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