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检索条件"主题词=evolutionary robotics"

共 635 条记录，以下是451-460 订阅

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Speeding Up Online Evolution of Robotic Controllers with Macro-neurons 17

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17th European Conference on Applications of evolutionary Computation (EvpApplications)

作者： Silva, Fernando Correia, Luis Christensen, Anders Lyhne Inst Telecomunicacoes Lisbon Portugal Inst Univ Lisboa ISCTE IUL Lisbon Portugal Univ Lisbon Fac Ciencias LabMAg Lisbon Portugal

ISBN: (纸本)9783662455234;9783662455227

In this paper, we introduce a novel approach to the online evolution of robotic controllers. We propose accelerating and scaling online evolution to more complex tasks by giving the evolutionary process direct access to behavioural building blocks prespecified in the neural architecture as macro-neurons. During task execution, both the structure and the parameters of macro-neurons and of the entire neural network are under evolutionary control. We perform a series of simulation-based experiments in which an e-puck-like robot must learn to solve a deceptive and dynamic phototaxis task with three light sources. We show that: (i) evolution is able to progressively complexify controllers by using the behavioural building blocks as a substrate, (ii) macro-neurons, either evolved or preprogrammed, enable a significant reduction in the adaptation time and the synthesis of high performing solutions, and (iii) evolution is able to inhibit the execution of detrimental task-unrelated behaviours and adapt non-optimised macro-neurons.

关键词： Online evolution evolutionary robotics Artificial neural network Prespecified behaviours Neuronal model

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Why and How to Measure Exploration in Behavioral Space 11

Why and How to Measure Exploration in Behavioral Space

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13th Annual Genetic and evolutionary Computation Conference (GECCO)

作者： Ollion, Charles Doncieux, Stephane UPMC ISIR Paris France

ISBN: (纸本)9781450305570

Exploration and exploitation are two complementary aspects of evolutionary Algorithms. Exploration, in particular, is promoted by specific diversity keeping mechanisms generally relying on the genotype or on the fitness value. Recent works suggest that, in the case of evolutionary robotics or more generally behavioral system evolution, promoting exploration directly in the behavioral space is of critical importance. In this work an exploration indicator is proposed, based on the sparseness of the population in the behavioral space. This exploration measure is used on two challenging neuro-evolution experiments and validated by showing the dependence of the fitness at the end of the run on the exploration measure during the very first generations. Such a prediction ability could be used to design parameter settings algorithms or selection algorithms dedicated to the evolution of behavioral systems. Several other potential uses of this measure are also proposed and discussed.

关键词： diversity evolutionary robotics evolutionary algorithm neuro-evolution exploration behavior

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A Fully-distributed Shape-aware Neural Controller for Modular Robots 23

A Fully-distributed Shape-aware Neural Controller for Modula...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Nadizar, Giorgia Medvet, Eric Walker, Kathryn Risi, Sebastian Univ Trieste DMG Trieste Italy Univ Trieste DIA Trieste Italy IT Univ Copenhagen Copenhagen Denmark

ISBN: (纸本)9798400701191

Modular robots are promising for their versatility and large design freedom. Modularity can also enable automatic assembly and reconfiguration, be it autonomous or via external machinery. However, these procedures are error-prone and often result in misassemblings. This, in turn, can cause catastrophic effects on the robot functionality, as the controller deployed in each module is optimized for a different robot shape than the actual one. In this work, we address such shortcoming by proposing a shape-aware modular controller, operating with (1) a self-discovery phase, in which each module controller identifies the shape it is assembled in, followed by (2) a parameter selection phase, where the controller selects its parameters according to the inferred shape. We deploy a self-classifying neural cellular automaton for phase (1), and we leverage evolutionary optimization for implementing a library of controller parameters for phase (2). We test the validity of the proposed method considering voxel-based soft robots, a class of modular soft robots, and the task of locomotion. Our findings confirm the effectiveness of such a controller paradigm, and also show that it can be used to partially overcome unforeseen damages or assembly mistakes.

关键词： evolutionary robotics Neural Cellular Automata Embodied Intelligence Neuroevolution Collective Intelligence

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Hybridizing Bio-Inspired Strategies with Infotaxis through Genetic Programming

Hybridizing Bio-Inspired Strategies with Infotaxis through G...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Macedo, Joao Marques, Lino Costa, Ernesto Univ Coimbra Coimbra Portugal

ISBN: (纸本)9781450392372

Locating odour sources with mobile robots is a difficult task with many applications. Over the years, researchers have devised bio-inspired and cognitive methods to enable mobile robots to fulfil this task. Cognitive approaches are effective in large spaces, but computationally heavy. On the other hand, bio-inspired ones are lightweight, but they are only effective in the presence of frequent stimuli. One of the most popular cognitive approaches is Infotaxis, which iteratively computes a probability map of the source location. Another strand of work uses Genetic Programming to produce complete search strategies from bio-inspired behaviours. This work combines the two approaches by allowing Genetic Programming to evolve search strategies that include infotactic and bio-inspired behaviours. The proposed method is tested in a set of environments with distinct airflow and chemical dispersion patterns. Its performance is compared to that of evolved strategies without infotactic behaviours and to the standard infotaxis approach. The statistically validated results show that the proposed method produces search strategies that have significantly higher success rates, whilst being faster than those produced by any of the original approaches. Moreover, the best evolved strategies are analysed, providing insight into when infotaxis is more beneficial.

关键词： evolutionary robotics Genetic Programming Odour Source Localisation robotics Infotaxis

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Evolving Complete Robots with CPPN-NEAT: The Utility of Recurrent Connections 11

Evolving Complete Robots with CPPN-NEAT: The Utility of Recu...

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13th Annual Genetic and evolutionary Computation Conference (GECCO)

作者： Auerbach, Joshua E. Bongard, Josh C. Univ Vermont Dept Comp Sci Morphol Evolut & Cognit Lab Burlington VT 05401 USA

ISBN: (纸本)9781450305570

This paper extends prior work using Compositional Pattern Producing Networks (CPPNs) as a generative encoding for the purpose of simultaneously evolving robot morphology and control. A method is presented for translating CPPNs into complete robots including their physical topologies, sensor placements, and embedded, closed-loop, neural network control policies. It is shown that this method can evolve robots for a given task. Additionally it is demonstrated how the performance of evolved robots can be significantly improved by allowing recurrent connections with in the underlying CPPNs. The resulting robots are analyzed in the hopes of answering why these recurrent connections prove to be so beneficial in this domain. Several hypotheses are discussed, some of which are refuted from the available data while others will require further examination.

关键词： CPPNs generative and developmental systems evolutionary robotics

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Impact of Neuron Models and Network Structure on Evolving Modular Robot Neural Network Controllers 12

Impact of Neuron Models and Network Structure on Evolving Mo...

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14th International Conference on Genetic and evolutionary Computation Conference (GECCO)

作者： Cazenille, Leo Bredeche, Nicolas Hamann, Heiko Stradner, Juergen Univ Paris 11 CNRS INRIA TAO LRI F-91405 Orsay France

ISBN: (纸本)9781450311779

This paper investigates the properties required to evolve Artificial Neural Networks for distributed control in modular robotics, which typically involves non-linear dynamics and complex interactions in the sensori-motor space. We investigate the relation between macro-scale properties (such as modularity and regularity) and micro-scale properties in Neural Network controllers. We show how neurons capable of multiplicative-like arithmetic operations may increase the performance of controllers in several ways whenever challenging control problems with non-linear dynamics are involved. This paper provides evidence that performance and robustness of evolved controllers can be improved by a combination of carefully chosen micro- and macro-scale neural network properties.

关键词： evolutionary robotics Artificial Neural Networks evolutionary Algorithms Modular robotics

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Biodiversity in Evolved Voxel-based Soft Robots

Biodiversity in Evolved Voxel-based Soft Robots

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2nd Genetic and evolutionary Computation Conference (GECCO)

作者： Medvet, Eric Bartoli, Alberto Pigozzi, Federico Rochelli, Marco Univ Trieste DIA Trieste Italy

ISBN: (纸本)9781450383509

In many natural environments, there are different forms of living creatures that successfully accomplish the same task while being diverse in shape and behavior. This biodiversity is what made life capable of adapting to disrupting changes. Being able to reproduce biodiversity in non-biological agents, while still optimizing them for a particular task, might increase their applicability to scenarios where human response to unexpected changes is not possible. In this work, we focus on Voxel-based Soft Robots (VSRs), a form of robots that grants great freedom in the design of both body and controller and is hence promising in terms of biodiversity. We use evolutionary computation for optimizing, at the same time, body and controller of VSRs for the task of locomotion. We investigate experimentally whether two key factors-evolutionary algorithm (EA) and representation-impact the emergence of biodiversity and if this occurs at the expense of effectiveness. We devise a way for measuring biodiversity, systematically characterizing the robots shape and behavior, and apply it to the VSRs evolved with three EAs and two representations. The experimental results suggest that the representation matters more than the EA and that there is not a clear trade-off between diversity and effectiveness.

关键词： evolutionary robotics Modularity Neuroevolution Representation Diversity

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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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How Do Different Encodings Influence the Performance of the MAP-Elites Algorithm? 16

How Do Different Encodings Influence the Performance of the ...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Tarapore, Danesh Clune, Jeff Cully, Antoine Mouret, Jean-Baptiste Univ York Dept Elect York YO10 5DD N Yorkshire England Univ Wyoming Evolving AI Lab Laramie WY 82071 USA Imperial Coll Personal Robot Lab London England Univ Lorraine CNRS UMR 7503 Inria Nancy Grand Est Nancy France

ISBN: (纸本)9781450342063

The recently introduced Intelligent Trial and Error algorithm (IT&E) both improves the ability to automatically generate controllers that transfer to real robots, and enables robots to creatively adapt to damage in less than 2 minutes. A key component of IT&E is a new evolutionary algorithm called MAP-Elites, which creates a behavior-performance map that is provided as a set of "creative" ideas to an online learning algorithm. To date, all experiments with MAP-Elites have been performed with a directly encoded list of parameters: it is therefore unknown how MAP-Elites would behave with more advanced encodings, like HyperNeat and SUPG. In addition, because we ultimately want robots that respond to their environments via sensors, we investigate the ability of MAP-Elites to evolve closed-loop controllers, which are more complicated, but also more powerful. Our results show that the encoding critically impacts the quality of the results of MAP-Elites, and that the differences are likely linked to the locality of the encoding (the likelihood of generating a similar behavior after a single mutation). Overall, these results improve our understanding of both the dynamics of the MAP-Elites algorithm and how to best harness MAP-Elites to evolve effective and adaptable robotic controllers.

关键词： MAP-Elites neuroevolution evolutionary robotics intelligent trial and error divergent search

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A Robot to Shape your Natural Plant: The Machine Learning Approach to Model and Control Bio-Hybrid Systems 18

A Robot to Shape your Natural Plant: The Machine Learning Ap...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Wahby, Mostafa Heinrich, Mary Katherine Hofstadler, Daniel Nicolas Zahadat, Payam Risi, Sebastian Ayres, Phil Schmickl, Thomas Hamann, Heiko Univ Lubeck Inst Comp Engn Lubeck Germany Royal Danish Acad Ctr IT & Architecture Copenhagen Denmark Karl Franzens Univ Graz Artificial Life Lab Graz Austria ITU Copenhagen Robot Evolut & Art Lab Copenhagen Denmark

ISBN: (纸本)9781450356183

Bio-hybrid systems-close couplings of natural organisms with technology-are high potential and still underexplored. In existing work, robots have mostly influenced group behaviors of animals. We explore the possibilities of mixing robots with natural plants, merging useful attributes. Significant synergies arise by combining the plants' ability to efficiently produce shaped material and the robots' ability to extend sensing and decision-making behaviors. However, programming robots to control plant motion and shape requires good knowledge of complex plant behaviors. Therefore, we use machine learning to create a holistic plant model and evolve robot controllers. As a benchmark task we choose obstacle avoidance. We use computer vision to construct a model of plant stem stiffening and motion dynamics by training an LSTM network. The LSTM network acts as a forward model predicting change in the plant, driving the evolution of neural network robot controllers. The evolved controllers augment the plants' natural light-finding and tissue-stiffening behaviors to avoid obstacles and grow desired shapes. We successfully verify the robot controllers and bio-hybrid behavior in reality, with a physical setup and actual plants.

关键词： bio-hybrid system natural plants decision making evolutionary robotics genetic algorithms neural networks

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