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检索条件"主题词=EVOLUTIONARY robotics"

共 634 条记录，以下是301-310 订阅

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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 ...

引用

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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Bridging the Reality Gap - A Dual Simulator Approach to the Evolution of Whole-Body Motion for the Nao Humanoid Robot 8

Bridging the Reality Gap - A Dual Simulator Approach to the ...

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8th International Joint Conference on Computational Intelligence

作者： Eaton, Malachy Univ Limerick Dept Comp Sci & Informat Syst Limerick Ireland

ISBN: (纸本)9789897582011

We describe a novel approach to the evolution of whole-body behaviours in the Nao humanoid robot using a multi-simulator approach to the alleviation of the reality gap issue. The initial evolutionary process takes place in the V-REP simulator. Once a viable whole-body motion has been evolved, this evolved motion is subsequently transferred for testing onto another simulation platform - Webots. Only when the evolved kicking behaviour has been demonstrated to also be viable on the Webots platform is this behaviour then transferred onto the real Nao robot for testing. This eliminates the time-consuming process of transferring behaviours onto the real robot which have little chance of successfully crossing the reality gap, and also minimises the potential for damage to the real Nao robot and/or it's environment. By using this novel approach of employing two different simulators, each with its own individual strengths and weaknesses, we reduce the likelihood that any individual behaviour will be able to exploit individual simulators' weaknesses, as the other simulator should pick up on this weak point. Using this procedure we have successfully evolved ball kicking behaviour in simulation, which has transferred with reasonable fidelity onto to the real Nao humanoid.

关键词： evolutionary Algorithms Humanoid robotics Ball Kicking evolutionary robotics evolutionary Humanoid robotics Whole-Body-Motion

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Sampling based Mission Planning for Multiple Robots

Sampling based Mission Planning for Multiple Robots

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IEEE Congress on evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Kala, Rahul Indian Inst Informat Technol Robot & Artificial Intelligence Lab Allahabad Uttar Pradesh India

ISBN: (纸本)9781509006229

Robots of the future would be sophisticated enough to do all kinds of tasks for the human master, while operating a team of robots would ensure that the tasks are done as efficiently as possible. This paper presents a solution to a very common mission wherein a team of robots needs to visit a number of mission sights and carry some operation there. The sights may have their own preference of robots. The real world workspace is first converted into a roadmap using the Probabilistic Roadmap algorithm. The adopted technique for roadmap generation uses a hybrid of narrow corridor sampler, obstacle based sampler and uniform sampler;with hybrid edge connection technique that aims to first connect disconnected roadmaps and then introduces redundant cycles and also ensures a good coverage of the configuration space. The roadmap is used to compute a cost matrix between every mission sight. This cost matrix is used by an optimization algorithm which deputes different mission sights to different robots as well as dictates the order of visit of the missions by each robot. Local optimization is used to quickly compute the optimal plan. The navigation of the robots is done using a Fuzzy Logic based navigator. Experiments show that the robots are able to coordinate with each other and complete the mission as a team very effectively.

关键词： evolutionary robotics Robot Mission Planning Fuzzy navigation Mission Optimization Sampling based robotics

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Evolving Neural Networks for Multi Robot Odor Search

Evolving Neural Networks for Multi Robot Odor Search

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IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

作者： Macedo, Joao Marques, Lino Costa, Ernesto Univ Coimbra Inst Syst & Robot Coimbra Portugal Univ Coimbra CISUC Coimbra Portugal

ISBN: (纸本)9781509022557

The tasks of odor detection, plume tracking and odor source localization constitute an important, yet complex, real world problem. One possible solution for them is based on the use of a group of mobile robots whose controllers have to be defined. Artificial Neural Networks (ANN) have already been used as controllers, but the task of hand defining their topology and parameters can be very challenging and time consuming. In this paper, we propose an approach to evolve, rather than design, ANN-based controllers. Our approach relies on Genetic Programming (GP), a family of stochastic search procedures loosely inspired by the biological principles of Natural Selection and Genetics. We compare our approach with a classic one, inspired by the chemotaxis behavior of the E. coli bacteria. Our results show that this approach is able to outperform the chemotaxis in the experiments performed.

关键词： Genetic Programming evolutionary robotics Artificial Neural Networks Multi Robot Odor Search

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Harnessing Phenotypic Diversity towards Multiple Independent Objectives

Harnessing Phenotypic Diversity towards Multiple Independent...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Smith, Davy Tokarchuk, Laurissa Wiggins, Geraint Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England

ISBN: (纸本)9781450343237

Multiple assessment directed novelty search (MADNS), introduced by the authors in [20], is an extension to the novelty search algorithm which exploits the observation that populations optimised for phenotypic novelty may contain solutions to multiple independent and conflicting objectives. It has been shown that, through the application of MADNS, an evolutionary trajectory may be simultaneously directed towards multiple conflicting objectives. Previous results from a series of simulated maze navigation experiments have shown that MADNS may significantly outperform novelty search in domains where the potential for phenotypic exploration is high [20]. In this paper we further explore the MADNS algorithm, assessing the effect upon the diversity and performance of the population as the phenotypic landscape increases. A series of experiments in domains with multiple conflicting objectives and expanding areas of irrelevant space show that the relative performance gain of MADNS increases alongside the potential for exploration. We conclude that, as the potential for exploration within a domain expands, the importance of directing novelty becomes ever more necessary.

关键词： Novelty search algorithm design phenotypic diversity neuroevolution evolutionary robotics

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Multi-objective Neuro-evolution: Should the Main Reproduction Mechanism be Crossover or Mutation?

Multi-objective Neuro-evolution: Should the Main Reproductio...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Salih, Adham Moshaiov, Amiram Tel Aviv Univ Sch Mech Engn Tel Aviv Israel ORT Braude Coll Engn Mech Engn Dept Karmiel Israel

ISBN: (纸本)9781509018970

Given the fundamental difference between the selection and reproduction mechanisms of MO-CMA-ES and NSGA-II, it should be asked which kind of these mechanisms is better for the multi-objective evolution of neuro-controllers. This question, which has been recently raised and studied, is further investigated here. The numerical investigation is based on two multi-objective navigation problems, in conjunction with two types of networks. In all the studied cases it was found that MO-CMA-ES is better than NSGA-II. The reason for the superiority is explored. First, it is shown that the competing convention problem cannot serve as an explanation to the observed phenomenon. A method is suggested to investigate the convergence of the networks. Based on the proposed methodology, it is found that for the studied cases MO-CMA-ES has a much better convergence properties. The differences between the two algorithms, and the uniqueness of the considered neuro-evolution problems, lead to the following hypothesis. It is postulated that MO-CMA-ES is superior as a result of its ability to fine-tune the solutions by changing particular genes, each at a time.

关键词： evolutionary neural-network neuroevolution evolutionary robotics multi-objective optimization

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Observing the Effects of Overdesign in the Automatic Design of Control Software for Robot Swarms 10th

Observing the Effects of Overdesign in the Automatic Design ...

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10th International Conference on Swarm Intelligence (ANTS)

作者： Birattari, Mauro Delhaisse, Brian Francesca, Gianpiero Kerdoncuff, Yvon Univ Libre Bruxelles IRIDIA Brussels Belgium ENSTA ParisTech Palaiseau France

ISBN: (纸本)9783319444277;9783319444260

We present the results of an experiment in the automatic design of control software for robot swarms. We conceived the experiment to corroborate a hypothesis that we proposed in a previous publication: the reality gap problem bears strong resemblance to the generalization problem faced in supervised learning. In particular, thanks to this experiment we observe for the first time a phenomenon that we shall call overdesign. Overdesign is the automatic design counterpart of the well known overfitting problem encountered in machine learning. Past an optimal level of the design effort, the longer the design process is protracted, the better the performance of the swarm becomes in simulation and the worst in reality. Our results show that some sort of early stopping mechanism could be beneficial.

关键词： Swarm robotics Automatic design evolutionary robotics Reality gap Generalization Overdesign Early stopping

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Exploring Conflicting Objectives with MADNS: Multiple Assessment Directed Novelty Search

Exploring Conflicting Objectives with MADNS: Multiple Assess...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Smith, Davy Tokarchuk, Laurissa Wiggins, Geraint Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England

ISBN: (纸本)9781450343237

Novelty search is an evolutionary approach which promotes phenotypic diversity in a population. Novelty search has been successfully applied to a wide range of domains and a number of variants have been proposed. Here we introduce Multiple Assessment Directed Novelty Search (MADNS), which exploits the notion that a diverse population optimised through phenotypic novelty may contain solutions to multiple conflicting objectives. We show that by utilising the MADNS algorithm, an evolutionary trajectory may be simultaneously directed towards conflicting objectives. We conclude that, through applying MADNS and MC-MADNS, a divergent evolutionary trajectory may be directed to provide simultaneous solutions to multiple conflicting problems in domains with large potential for exploration.

关键词： Novelty search algorithm design phenotypic diversity neuroevolution evolutionary robotics

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Biophilic evolutionary Buildings that Restore the Experience of Animality in the City 4th

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4th International Conference on Biomimetic and Biohybrid Systems (Living Machines)

作者： Gil, Pablo Rossi, Claudio Coral, William Barlett Sch Architecture London England Univ Europea Madrid Madrid Spain Univ Politecn Madrid Ctr Automat & Robot UPM CSIC Madrid Spain

ISBN: (纸本)9783319229799;9783319229782

In this paper, we present our work on the training of robotised architectural components of intelligent buildings, focusing on how architectural components can learn to behave animalistically, according to the judgment of human users. Our work aims at recovering the lost contact with animals in the urban context, taking advantage of biophilic empathy. The parameters governing the robotised elements we propose are mainly qualitative (emotions and aesthetical perception), which cannot easily be described by mathematical parameters. Additionally, due to their complexity, it is often impossible - or at least impractical, to hardcode suitable controllers for such structures. Thus, we propose the use of Artificial Intelligence learning techniques, concretely evolutionary Algorithms, to allow the user to teach the robotised components how to behave in response to their resemblance to specific animal behaviors. This idea is tested on an intelligent fa, cade that learns optimal configurations according to the perception of aggressiveness and calmness.

关键词： Biophilia Biomimicry Wellbeing evolutionary robotics Embodied evolution Intelligent buildings

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Improving Survivability in Environment-driven Distributed evolutionary Algorithms through Explicit Relative Fitness and Fitness Proportionate Communication 15

Improving Survivability in Environment-driven Distributed Ev...

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17th Genetic and evolutionary Computation Conference (GECCO)

作者： Hart, Emma Steyven, Andreas Paechter, Ben Edinburgh Napier Univ Sch Comp Edinburgh Midlothian Scotland

ISBN: (纸本)9781450334723

Ensuring the integrity of a robot swarm in terms of maintaining a stable population of functioning robots over long periods of time is a mandatory prerequisite for building more complex systems that achieve user-defined tasks. mEDEA is an environment-driven evolutionary algorithm that provides promising results using an implicit fitness function combined with a random genome selection operator. Motivated by the need to sustain a large population with sufficient spare energy to carry out user-defined tasks in the future, we develop an explicit fitness metric providing a measure of fitness that is relative to surrounding robots and examine two methods by which it can influence spread of genomes. Experimental results in simulation find that use of the fitness-function provides significant improvements over the original algorithm;in particular, a method that influences the frequency and range of broadcasting when combined with random selection has the potential to conserve energy whilst maintaining performance, a critical factor for physical robots.

关键词： evolutionary robotics Environment-driven On-line Evolution

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