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检索条件"主题词=evolutionary robotics"

共 635 条记录，以下是421-430 订阅

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Evolving-Controllers Versus Learning-Controllers for Morphologically Evolvable Robots 1

引用

23rd European International Conference on the Applications of evolutionary Computation (EvoApplications) Held as Part of EvoStar Conference

作者： Miras, Karine De Carlo, Matteo Akhatou, Sayfeddine Eiben, A. E. Vrije Univ Amsterdam Amsterdam Netherlands

ISBN: (数字)9783030437220

ISBN: (纸本)9783030437213;9783030437220

We investigate an evolutionary robot system where (simulated) modular robots can reproduce and create robot children that inherit the parents' morphologies by crossover and mutation. Within this system we compare two approaches to creating good controllers, i.e., evolution only and evolution plus learning. In the first one the controller of a robot child is inherited, so that it is produced by applying crossover and mutation to the controllers of its parents. In the second one the controller of the child is also inherited, but additionally, it is enhanced by a learning method. The experiments show that the learning approach does not only lead to different fitness levels, but also to different (bigger) robots. This constitutes a quantitative demonstration that changes in brains, i.e., controllers, can induce changes in the bodies, i.e., morphologies.

关键词： Morphological evolution Life-time learning evolutionary robotics Modular robots

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A colony of robots using vision sensing and evolved neural controllers

A colony of robots using vision sensing and evolved neural c...

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Nelson, AL Grant, E Barlow, GJ Henderson, TC N Carolina State Univ Dept Elect & Comp Engn Ctr Robot & Intelligence Machines Raleigh NC 27695 USA

ISBN: (纸本)0780378601

This paper describes the development and testing of a new evolutionary robotics research test bed. The test bed consists of a colony of small computationally powerful mobile robots that use evolved neural network controllers and vision based sensors to generate team game-playing behaviors. The vision based sensors function by converting video images into range and object color data. Large evolvable neural network controllers use these sensor data to control mobile robots. The networks require 150 individual input connections to accommodate the processed video sensor data. Using evolutionary computing methods, the neural network based controllers were evolved to play the competitive team game Capture the Flag with teams of mobile robots. Neural controllers were evolved in simulation and transferred to real robots for physical verification. Sensor signals in the simulated environment are formatted to duplicate the processed real video sensor values rather than the raw video images. Robot controllers receive sensor signals and send actuator commands of the same format, whether they are driving physical robots in a real environment or simulated robots agents in an artificial environment. Evolved neural controllers can be transferred directly to the real mobile robots for testing and evaluation. Experimental results generated with this new evolutionary robotics research test bed are presented.

关键词： evolutionary robotics robot colonies mobile robots evolutionary neural computing behavioral robotics vision robot vision

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Towards Crossing the Reality Gap with Evolved Plastic Neurocontrollers

Towards Crossing the Reality Gap with Evolved Plastic Neuroc...

引用

Genetic and evolutionary Computation Conference (GECCO)

作者： Qiu, Huanneng Garratt, Matthew Howard, David Anavatti, Sreenatha Univ New South Wales Canberra Canberra ACT Australia CSIRO Robot & Autonomous Syst Grp Brisbane Qld Australia

ISBN: (纸本)9781450371285

A critical issue in evolutionary robotics is the transfer of controllers learned in simulation to reality. This is especially the case for small Unmanned Aerial Vehicles (UAVs), as the platforms are highly dynamic and susceptible to breakage. Previous approaches often require simulation models with a high level of accuracy, otherwise significant errors may arise when the well-designed controller is being deployed onto the targeted platform. Here we try to overcome the transfer problem from a different perspective, by designing a spiking neurocontroller which uses synaptic plasticity to cross the reality gap via online adaptation. Through a set of experiments we show that the evolved plastic spiking controller can maintain its functionality by self-adapting to model changes that take place after evolutionary training, and consequently exhibit better performance than its non-plastic counterpart.

关键词： evolutionary robotics spiking neural networks Hebbian plasticity neuroevolution UAV control

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Effects of Encodings and Quality-diversity on Evolving 2D Virtual Creatures 22

Effects of Encodings and Quality-diversity on Evolving 2D Vi...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Veenstra, Frank Olsen, Martin Herring Glette, Kyrre Univ Oslo Oslo Norway

ISBN: (纸本)9781450392686

How to jointly optimize the morphology and controller is a challenging problem in evolutionary robotics. Due to the large search space, both quality diversity algorithms and types of encodings have been employed to search the solution space more effectively. Here we compare Multi-dimensional Archive of Phenotypic Elites (MAP-Elites) and a standard evolutionary algorithm as well as the effect of using a direct versus an indirect encoding. The results showed that the MAP-Elites algorithm found diverse solutions, yet the encodings accounted for a larger performance discrepancy. This indicates that the representation is at least as important as the optimization method for effectively creating robots.

关键词： evolutionary robotics Virtual Creatures Encodings Quality-diversity

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Modular Controllers Facilitate the Co-Optimization of Morphology and Control in Soft Robots 23

Modular Controllers Facilitate the Co-Optimization of Morpho...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Mertan, Alican Cheney, Nick Univ Vermont Neurobot Lab Burlington VT 05405 USA

ISBN: (纸本)9798400701191

Soft robotics is a rapidly growing area of robotics research that would benefit greatly from design automation, given the challenges of manually engineering complex, compliant, and generally non-intuitive robot body plans and behaviors. It has been suggested that a major hurdle currently limiting soft robot brain-body co-optimization is the fragile specialization between a robot's controller and the particular body plan it controls, resulting in premature convergence. Here we posit that modular controllers are more robust to changes to a robot's body plan. We demonstrate a decreased reduction in locomotion performance after morphological mutations to soft robots with modular controllers, relative to those with similar global controllers - leading to fitter offspring. Moreover, we show that the increased transferability of modular controllers to similar body plans enables more effective brain-body co-optimization of soft robots, resulting in an increased rate of positive morphological mutations and higher overall performance of evolved robots. We hope that this work helps provide specific methods to improve soft robot design automation in this particular setting, while also providing evidence to support our understanding of the challenges of brain-body co-optimization more generally.

关键词： evolutionary robotics soft robotics brain-body co-optimization

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Nonlinear Dynamics Modelling for Controller Evolution 07

Nonlinear Dynamics Modelling for Controller Evolution

引用

Annual Conference of Genetic and evolutionary Computation Conference

作者： Togelius, Julian De Nardi, Renzo Marques, Hugo Newcombe, Richard Lucas, Simon M. Holland, Owen Univ Essex Dept Comp Sci Colchester CO4 3SQ Essex England

ISBN: (纸本)9781595936974

The problem of how to acquire a model of a physical robot, which is fit for evolution of controllers that can subsequently be used to control that robot, is considered in the context, of racing a radio-controlled toy car around a randomised track. Several modelling techniques are compared, and the specific properties of the acquired models that;influence the quality of the evolved controller are discussed. As we ann to minimise the amount;of domain knowledge used., we further investigate the relation between the assumptions about the modelled system made by particular modelling techniques and the suitability of the acquired models as bases for controller evolution. We find that none of the models acquired is good enough on its own, and that a key, to evolving robust behaviour is to evaluate controllers simultaneously on multiple models during evolution. Examples of successfully evolved racing control for the physical car are analysed.

关键词： evolutionary robotics neural networks forward models system identification games

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The Dependence of Braking Strategies on Optical Variables in an Evolved Model of Visually-Guided Braking

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11th International Conference on Simulation of Adaptive Behavior

作者： Kadihasanoglu, Didem Beer, Randall D. Bingham, Geoffrey P. Indiana Univ Cognit Sci Program Bloomington IN 47406 USA

ISBN: (纸本)9783642151927

This paper presents results from two sets of experiments which investigate how strategies used by embodied dynamical agents in a simple braking task are affected by the perceptual information that the agents receive. Agents are evolved in a simple 2D environment containing one stationary object. The task of the agents is to stop as close as possible to the object without hitting it. The results of these experiments demonstrate that most of the evolved agents use an impulsive braking strategy, in which deceleration is not controlled continuously. Potential causes of this impulsive braking strategy and possible future directions are discussed.

关键词： evolutionary robotics visually-guided braking image size image expansion rate tau tau-dot

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Devising Effective Novelty Search Algorithms: A Comprehensive Empirical Study 15

Devising Effective Novelty Search Algorithms: A Comprehensiv...

引用

17th Genetic and evolutionary Computation Conference (GECCO)

作者： Gomes, Jorge Mariano, Pedro Christensen, Anders Lyhne Univ Lisbon BioISI BioMachines Lab P-1699 Lisbon Portugal Univ Lisbon BioISI Inst Telecomunicacoes P-1699 Lisbon Portugal Univ Lisbon Fac Ciencias BioISI Lisbon Portugal BioMachines Lab Lisbon Portugal Inst Telecomunicacoes Lisbon Portugal Inst Univ Lisboa ISCTE IUL Lisbon Portugal

ISBN: (纸本)9781450334723

Novelty search is a state-of-the-art evolutionary approach that promotes behavioural novelty instead of pursuing a static objective. Along with a large number of successful applications, many different variants of novelty search have been proposed. It is still unclear, however, how some key parameters and algorithmic components influence the evolutionary dynamics and performance of novelty search. In this paper, we conduct a comprehensive empirical study focused on novelty search's algorithmic components. We study the k parameter - the number of nearest neighbours used in the computation of novelty scores;the use and function of an archive;how to combine novelty search with fitness-based evolution;and how to con figure the mutation rate of the underlying evolutionary algorithm. Our study is conducted in a simulated maze navigation task. Our results show that the con figuration of novelty search can have a significant impact on performance and behaviour space exploration. We conclude with a number of guidelines for the implementation and con figuration of novelty search, which should help future practitioners to apply novelty search more effectively.

关键词： Novelty search evolutionary robotics neuroevolution premature convergence empirical study

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Dynamics of Neuronal Models in Online Neuroevolution of Robotic Controllers

Dynamics of Neuronal Models in Online Neuroevolution of Robo...

引用

16th Portuguese Conference on Artificial Intelligence (EPIA)

作者： Silva, Fernando Correia, Luis Christensen, Anders Lyhne Inst Telecomunicacoes Lisbon Portugal Univ Lisbon ISCTE P-1200 Lisbon Portugal Univ Lisbon Fac Ciencias LabMAg P-1200 Lisbon Portugal

ISBN: (纸本)9783642406690;9783642406683

In this paper, we investigate the dynamics of different neuronal models on online neuroevolution of robotic controllers in multirobot systems. We compare the performance and robustness of neural network-based controllers using summing neurons, multiplicative neurons, and a combination of the two. We perform a series of simulation-based experiments in which a group of e-puck-like robots must perform an integrated navigation and obstacle avoidance task in environments of different complexity. We show that: (i) multiplicative controllers and hybrid controllers maintain stable performance levels across tasks of different complexity, (ii) summing controllers evolve diverse behaviours that vary qualitatively during task execution, and (iii) multiplicative controllers lead to less diverse and more static behaviours that are maintained despite environmental changes. Complementary, hybrid controllers exhibit both behavioural characteristics, and display superior generalisation capabilities in simple and complex tasks.

关键词： evolutionary robotics artificial neural network evolutionary algorithm online neuroevolution

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Stability of coordination requires mutuality of interaction in a model of embodied agents

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10th International Conference on Simulation of Adaptive Behavior

作者： Froese, Tom Di Paolo, Ezequiel A. Univ Sussex Dept Informat CCNR Brighton BN1 9RH E Sussex England

ISBN: (纸本)9783540691334

We used an evolutionary robotics methodology to generate pairs of simulated agents capable of reliably establishing and maintaining a coordination pattern under noisy conditions. Unlike previous related work, agents were only evolved for this ability and not for their capacity to discriminate social contingency (i.e., a live responsive partner) from non-contingent engagements (i.e., a recording). However, when they were made to interact with a recording of their partner made during a successful previous interaction, the coordination pattern could not be established. An analysis of the system's underlying dynamics revealed (i) that stability of the coordination pattern requires ongoing mutuality of interaction, and (ii) that the interaction process is not only constituted by, but also constitutive of, individual behavior. We suggest that this stability of coordination is a general property of a certain class of interactively coupled dynamical systems, and conclude that psychological explanations of an individual's sensitivity to social contingency need to take into account the role of the interaction process.

关键词： evolutionary robotics social cognition social contingency

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