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检索条件"主题词=evolutionary robotics"

共 635 条记录，以下是561-570 订阅

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An Approach to Representation Learning in Morphological Robot Evolution

An Approach to Representation Learning in Morphological Robo...

引用

2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Stuurman, Aart C. Yaman, Anil Eiben, A.E. Vrije Universiteit Amsterdam Netherlands

ISBN: (纸本)9781665430654

A key challenge for evolving complex physical ob-jects is to design a representation, that is, to devise suitable genotypes and a good mapping from genotypes to phenotypes (the objects to be evolved). This paper outlines a new approach to address this challenge for evolving robot morphologies and presents a proof-of-concept study to assess its feasibility. The key idea is to design genotype-phenotype mappings using variational autoencoders. This idea is implemented and tested for the evolution of modular robots for a locomotion task. The experiments show the practicability of this idea where the representation is not hand-designed, but algorithmically generated. This indicates a great future potential for the evolution of complex objects where there are no straightforward representations to use. © 2023 IEEE.

关键词： evolutionary robotics genotype-phenotype mapping morphological evolution representation

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A Comparison of Controller Architectures and Learning Mechanisms for Arbitrary Robot Morphologies

A Comparison of Controller Architectures and Learning Mechan...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Luo, Jie Tomczak, Jakub Miras, Karine Eiben, Agoston E. Vrije Universiteit Amsterdam Computer Science Dept. Netherlands Eindhoven University of Technology Computer Science Dept. Netherlands

ISBN: (纸本)9781665430654

The main question this paper addresses is: What combination of a robot controller and a learning method should be used, if the morphology of the learning robot is not known in advance? Our interest is rooted in the context of morphologically evolving modular robots, but the question is also relevant in general, for system designers interested in widely applicable solutions. We perform an experimental comparison of three controller-and-learner combinations: one approach where controllers are based on modelling animal locomotion (Central Pattern Generators, CPG) and the learner is an evolutionary algorithm, a completely different method using Reinforcement Learning (RL) with a neural network controller architecture, and a combination 'in-between' where controllers are neural networks and the learner is an evolutionary algorithm. We apply these three combinations to a test suite of modular robots and compare their efficacy, efficiency, and robustness. Surprisingly, the usual CPG-based and RL-based options are outperformed by the in-between combination that is more robust and efficient than the other two setups. © 2023 IEEE.

关键词： controller CPG evolutionary robotics learning algorithm Reinforcement learning

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Levels of Coupling in Dyadic Interaction: An Analysis of Neural and Behavioral Complexity

Levels of Coupling in Dyadic Interaction: An Analysis of Neu...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Resendiz-Benhumea, Georgina Montserrat Sangati, Ekaterina Froese, Tom Okinawa Inst Sci & Technol Grad Univ Embodied Cognit Sci Unit Onna Okinawa Japan Univ Nacl Autonoma Mexico Inst Appl Math & Syst Res Mexico City DF Mexico

ISBN: (纸本)9781728125473

From an enactive approach, some previous studies have demonstrated that social interaction plays a fundamental role in the dynamics of neural and behavioral complexity of embodied agents. In particular, it has been shown that agents with a limited internal structure (2-neuron brains) that evolve in interaction can overcome this limitation and exhibit chaotic neural activity, typically associated with more complex dynamical systems (al least 3-dimensional). In the present paper we make two contributions to this line of work. First, we propose a conceptual distinction in levels of coupling between agents that could have an effect on neural and behavioral complexity. Second, we test the generalizability of previous results by testing agents with richer internal structure and evolving them in a richer, yet non-social, environment. We demonstrate that such agents can achieve levels of complexity comparable to agents that evolve in interactive settings. We discuss the significance of this result for the study of interaction.

关键词： agent-based modeling social interaction coupling neural entropy evolutionary robotics continuous-time recurrent neural network minimal cognition dyad

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Automated Mechanism Design: Introducing Reduced Operator-Space Evolution

Automated Mechanism Design: Introducing Reduced Operator-Spa...

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作者： Westra, Koos Delft University of Technology

学位级别：硕士

Previous research has shown automated robotic mechanism design to be both deceptive (prone to local minima) and rife with linkage problems (having highly interdependent parameters). This results in a barrier to optimization that is unable to be breached by simply applying more iterations and computational power. The research also indicates that a graph structure model of the robot in combination with an evolutionary algorithm yields useful robotic mechanisms for a limited set of simple problems. This thesis expands on this pre-existing representation by introducing an indirect model that can be used to include both controllers, motors and other new elements in the representation. Besides this extension of the mechanism model, a framework for the automated design optimization task itself is introduced. This thesis shows an equivalence between an operator based representation of the mechanisms and the graph based representation. These operators represent modifications on the mechanism structure and/or parameters. By recognizing the operators as paths in this model a graph of the search space itself can be constructed. In this graph the vertices are mechanisms and the edges are operators. Using the the operator-mechanism equivalence it is shown that designing an optimization algorithm is equivalent to (1) choosing how vertices in the space are grouped together. (2) choosing how the vertices of this search space are connected beforehand by either implicitly or explicitly picking operators and projecting onto their corresponding domain. (3) picking which of the connected paths to traverse based on accumulated information at runtime. This represents a framework that allows the accumulation of knowledge about optimization algorithms acting within it by defining a set of meta-heuristics. With these it is possible to make informed choices to build better optimization algorithms. To show the effectiveness of the framework a novel quality diversity algorithm is developed, Redu

关键词： Mechanism Design Optimization Algorithm evolutionary Algorithms evolutionary robotics

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Coupling Evolution and Information Theory for Autonomous Robotic Exploration

Coupling Evolution and Information Theory for Autonomous Rob...

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13th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Zhang, Guohua Sebag, Michele Chinese Acad Sci Chengdu Inst Comp Applicat Chengdu 610041 Peoples R China Univ Paris 11 CNRS INRIA LRI TAO F-91128 Orsay France

ISBN: (纸本)9783319107622;9783319107615

This paper investigates a hybrid two-phase approach toward exploratory behavior in robotics. In a first phase, controllers are evolved to maximize the quantity of information in the sensori-motor datastream generated by the robot. In a second phase, the data acquired by the evolved controllers is used to support an information theory-based controller, selecting the most informative action in each time step. The approach, referred to as EvITE, is shown to outperform both the evolutionary and the information theory-based approaches standalone, in terms of actual exploration of the arena. Further, the EvITE controller features some generality property, being able to efficiently explore other arenas than the one considered during the first evolutionary phase.

关键词： evolutionary robotics information theory intrinsic motivation entropy

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evolutionary Mission Planning

Evolutionary Mission Planning

引用

IEEE Congress on evolutionary Computation

作者： Rahul Kala Abeer Khan D. Diksha S. Shelly Surabhi Sinha Robotics and Artificial Intelligence Laboratory Indian Institute of Information Technology Allahabad Allahabad India

The problem of mission planning enables a robot to solve for complex missions and thereafter execute the missions. The problem is seen as an advancement of the classical problem of motion planning wherein the task is to find a trajectory for a robot to go from a configuration A to a configuration B avoiding obstacles. The popular mechanism to solve the problem is using Temporal Logic specifications to specify a mission, and to thereafter use search strategies to solve the mission. The same requires an exponential complexity in terms of propositional variables to search and verify the mission plan. Further, optimality is usually not a criterion in finding a solution, and the transition costs are usually ignored leading to sub-optimal mission plans. As missions get more complex using a large number of propositional variables for specification, it may no longer be possible to use exponential complexity algorithms. The paper proposes the use of evolutionary Computation to solve the same problem. We first develop a new restricted language for mission design. Even though the language is restrictive, it can specify a large number of missions of real life service robotics. Then we design an evolutionary computation framework to solve the mission. Probabilistic Roadmap technique is used to get the transition system and transition costs between regions of interest. The mission planner takes the mission specification and these transition costs to compute a mission plan. The mission plan is executed using a reactive navigator that can avoid any dynamic obstacle, other people and robots.

关键词： evolutionary robotics Genetic Algorithm Robot Motion Planning Service robotics

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Open-Endedness Induced Through a Predator-Prey Scenario Using Modular Robots

Open-Endedness Induced Through a Predator-Prey Scenario Usin...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Kachler, Dimitri Miras, Karine Vrije Universiteit Amsterdam Computer Science Dept. Amsterdam Netherlands

ISBN: (纸本)9781665430654

This work investigates how a predator-prey scenario can induce the emergence of Open-Ended Evolution (OEE). We utilize modular robots of fixed morphologies whose controllers are subject to evolution. In both species, robots can send and receive signals and perceive the relative positions of other robots in the environment. Specifically, we introduce a feature we call a tagging system: it modifies how individuals can perceive each other and is expected to increase behavioral complexity. Our results show the emergence of adaptive strategies, demonstrating the viability of inducing OEE through predator-prey dynamics using modular robots. Such emergence, nevertheless, seemed to depend on conditioning reproduction to an explicit behavioral criterion. © 2023 IEEE.

关键词： evolutionary robotics Modular Robots Open-Ended Evolution Predator-Prey

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Exploring The Modularity And Structure Of Robots Evolved In Multiple Environments

Exploring The Modularity And Structure Of Robots Evolved In ...

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作者： Collin Cappelle University of Vermont

学位级别：博士

Traditional techniques for the design of robots require human engineers to plan every aspect of the system, from body to controller. In contrast, the field of evolu- tionary robotics uses evolutionary algorithms to create optimized morphologies and neural controllers with minimal human intervention. In order to expand the capability of an evolved agent, it must be exposed to a variety of conditions and environments. This thesis investigates the design and benefits of virtual robots which can reflect the structure and modularity in the world around them. I show that when a robotâs morphology and controller enable it to perceive each environment as a collection of independent components, rather than a monolithic entity, evolution only needs to optimize on a subset of environments in order to maintain performance in the overall larger environmental space. I explore previously unused methods in evolutionary robotics to aid in the evolution of modularity, including using morphological and neurological cost. I utilize a tree morphology which makes my results generalizable to other mor- phologies while also allowing in depth theoretical analysis about the properties rel- evant to modularity in embodied agents. In order to better frame the question of modularity in an embodied context, I provide novel definitions of morphological and neurological modularity as well as create the sub-goal interference metric which mea- sures how much independence a robot exhibits with regards to environmental stimu- lus. My work extends beyond evolutionary robotics and can be applied to the opti- mization of embodied systems in general as well as provides insight into the evolution of form in biological organisms.

关键词： Artificial Intelligence evolutionary Computation evolutionary robotics Modularity Computer Sciences

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Incentive for Self-Protection in a Collective System: a Swarm robotics Case Study

Incentive for Self-Protection in a Collective System: a Swar...

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2021 Conference on Artificial Life, ALIFE 2021

作者： Zahadat, Payam IT University of Copenhagen Denmark

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Novelty Search in Competitive Coevolution

Novelty Search in Competitive Coevolution

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13th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Gomes, Jorge Mariano, Pedro Christensen, Anders Lyhne Inst Telecomunicacoes Lisbon Portugal Univ Lisbon Fac Ciencias LabMAg Lisbon Portugal Inst Univ Lisboa ISCTE IUL Lisbon Portugal

ISBN: (纸本)9783319107622;9783319107615

One of the main motivations for the use of competitive coevolution systems is their ability to capitalise on arms races between competing species to evolve increasingly sophisticated solutions. Such arms races can, however, be hard to sustain, and it has been shown that the competing species often converge prematurely to certain classes of behaviours. In this paper, we investigate if and how novelty search, an evolutionary technique driven by behavioural novelty, can overcome convergence in coevolution. We propose three methods for applying novelty search to coevolutionary systems with two species: (i) score both populations according to behavioural novelty;(ii) score one population according to novelty, and the other according to fitness;and (iii) score both populations with a combination of novelty and fitness. We evaluate the methods in a predator-prey pursuit task. Our results show that novelty-based approaches can evolve a significantly more diverse set of solutions, when compared to traditional fitness-based coevolution.

关键词： Competitive coevolution behavioural diversity novelty search convergence evolutionary robotics

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