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4th IEEE International Conference on Development and Learning

作者： Bredeche, N Hugues, L Univ Paris Sud XI Rech Informat Lab TAO INRIA F-91405 Orsay France

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ISBN: (纸本)0780392256

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关键词： evolutionary robotics dynamic fitness

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evolutionary robotics and the radical envelope-of-noise hypothesis

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ADAPTIVE BEHAVIOR 1997年第2期6卷 325-368页

作者： Jakobi, N Univ Sussex Sch Cognit & Comp Sci Brighton BN1 9QH E Sussex England

For several years now, various researchers have endeavored to apply artificial evolution to the automatic design of control systems for real robots. One of the major challenges they face concerns the question of how to assess the fitness of evolving controllers when each evolutionary run typically involves hundreds of thousands of such assessments. This article outlines new ways of thinking about and building simulations upon which such assessments can be performed. it puts forward sufficient conditions for the successful transfer of evolved controllers from simulation to reality and develops a potential methodology for building simulations in which evolving controllers are forced To satisfy these conditions if they are to be reliably fit. it is hypothesized that as long as simulations are built according to this methodology, it does not matter how inaccurate or incomplete they are: Controllers that have evolved to be reliably fit in simulation still will transfer into reality. Two sets of experiments are reported both of which involve minimal look-up fable-based simulations built according to these guidelines. in the first set, controllers were evolved that allowed a Khepera robot to perform a simple memory task in the real world. in the second set, controllers were evolved for the Sussex University gantry robot that were able so distinguish visually a triangle from a square, under extremely noisy real-world conditions, and to steer the robot toward the triangle. in both cases, controllers that were reliably fit in simulation displayed extremely robust behavior when downloaded into reality.

关键词： simulations evolutionary robotics robot-environment interactions neural networks

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evolutionary robotics for Legged Machines: From Simulation to Physical Reality

Evolutionary Robotics for Legged Machines: From Simulation t...

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9th International Conference on Intelligent Autonomous Systems (IAS-9)

作者： Lipson, Hod Bongard, Josh Zykov, Victor Malone, Evan Cornell Univ Computat Synth Lab Ithaca NY 14853 USA

This talk will outline challenges and opportunities in translating evolutionary learning of autonomous robotics from simulation to reality. It covers evolution and adaptation of both morphology and control, hybrid co-... 详细信息

ISBN: (纸本)9781586035952

关键词： evolutionary robotics co-evolutionary learning estimation-exploration rapid prototyping

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evolutionary robotics Applied to the Multi-Robot Worst-Case Pursuit-Evasion Problem 7

Evolutionary Robotics Applied to the Multi-Robot Worst-Case ...

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7th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (IEEE UEMCON)

作者： Gregorin, Livia Freire, Eduardo Carvalho, Elyson Molina, Lucas Givigi, Sidney Univ Fed Sergipe Dept Elect Engn Aracaju Brazil Royal Mil Coll Canada Dept Elect & Comp Engn Kingston ON Canada

ISBN: (纸本)9781509014965

This paper proposes the use of an evolutionary robotics approach to solve the worst-case pursuit-evasion problem, in which evaders are considered arbitrarily fast and omniscient, while pursuers have limited sensing and communication capabilities, with no previous knowledge of the environment. Unlike most work in evolutionary robotics, we offer a control system for multiple mobile robots based on finite state machines derived using a genetic algorithm. Results show that, given a sufficient number of robots, the evolved system is capable of clearing a discrete map, including multiply connected maps, of all previous contamination.

关键词： evolutionary robotics finite state machines pursuit-evasion

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evolutionary robotics on Lego NXT Platform 27

Evolutionary Robotics on Lego NXT Platform

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27th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Poikselka, Katja Vallivaara, Ilari Roning, Juha Univ Oulu Dept Comp Sci & Engn SF-90100 Oulu Finland

ISBN: (纸本)9781509001637

This paper studies the Lego NXT platform's suitability for evolutionary robotics. It is shown that the low-cost Lego NXT educational set is indeed adequate for simple experiments in evolutionary robotics. This is demonstrated by an experiment, where an artificial neural network-based controller capable of behaving meaningfully in a Lego sumo wrestling context is evolved on physical Lego NXT robots without the aid of simulation. A detailed description of the experiment is provided, and the practical aspects of actually conducting evolutionary robotics on the platform are studied. Earlier research suggests that using evolutionary robotics in education could provide a good and concrete example of the principles and mechanisms of evolution. The research described here utilizes only standard Lego NXT Educational kits, making conducting the experiments possible for a very wide audience. To the authors' knowledge this is the first time the non-simulated Lego NXT is used to conduct artificial neural network-based evolutionary robotics.

关键词： evolutionary robotics Neural networks Lego NXT

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Lexicase Selection for Multi-Task evolutionary robotics

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ARTIFICIAL LIFE 2022年第4期28卷 479-498页

作者： Stanton, Adam Moore, Jared M. Aston Univ Sch Informat & Digital Engn Birmingham W Midlands England Grand Valley State Univ Sch Comp Allendale MI 49401 USA

In evolutionary robotics, Lexicase selection has proven effective when a single task is broken down into many individual parameterizations. Evolved individuals have generalized across unique configurations of an overarching task. Here, we investigate the ability of Lexicase selection to generalize across multiple tasks, with each task again broken down into many instances. There are three objectives: to determine the feasibility of introducing additional tasks to the existing platform;to investigate any consequential effects of introducing these additional tasks during evolutionary adaptation;and to explore whether the schedule of presentation of the additional tasks over evolutionary time affects the final outcome. To address these aims we use a quadruped animat controlled by a feed-forward neural network with joint-angle, bearing-to-target, and spontaneous sinusoidal inputs. Weights in this network are trained using evolution with Lexicase-based parent selection. Simultaneous adaptation in a wall crossing task (labelled wall-cross) is explored when one of two different alternative tasks is also present: turn-and-seek or cargo-carry. Each task is parameterized into 100 distinct variants, and these variants are used as environments for evaluation and selection with Lexicase. We use performance in a single-task wall-cross environment as a baseline against which to examine the multi-task configurations. In addition, the objective sampling strategy (the manner in which tasks are presented over evolutionary time) is varied, and so data for treatments implementing uniform sampling, even sampling, or degrees of generational sampling are also presented. The Lexicase mechanism successfully integrates evolution of both turn-and-seek and cargo-carry with wall-cross, though there is a performance penalty compared to single task evolution. The size of the penalty depends on the similarity of the tasks. Complementary tasks (wallcross/turn-and-seek) show better performance than a

关键词： Multi-objective many-objective evolutionary robotics lexicase selection transfer learning

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Concurrent controller and Simulator Neural Network development for a snake-like robot in evolutionary robotics

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robotics AND AUTONOMOUS SYSTEMS 2017年 88卷 37-50页

作者： Woodford, Grant W. du Plessis, Mathys C. Pretorius, Christiaan J. NMMU Dept Comp Sci Port Elizabeth South Africa NMMU Dept Math & Appl Math Port Elizabeth South Africa

evolutionary robotics (ER) is a field of study that has shown much promise in automating the development of robotic controllers and morphologies. The use of simulators as an alternative to real-world robots is often employed to reduce the time required to develop effective controllers in the ER process. However, the development of adequate simulators is often time-consuming and complex. Simulators are usually constructed from physics models and/or are based on empirically collected data. The vast majority of simulation approaches are based on physics models which can become complex and require specialised knowledge. Alternative simulation approaches that simplify and automate the modelling of real-world phenomena can provide certain advantages over traditional approaches. An alternative simulation approach, such as Artificial Neural Networks (ANNs) that model the real-world phenomena based on empirical data are relatively simple to construct and requires little specialised knowledge. ANN-based simulators are traditionally constructed before the ER process can begin and require the sampling of real-world experimental data. Disadvantages to the traditional approach to ANN-based simulator construction are that the simulator must be created before the ER process can be initiated and a large amount of behavioural data must be collected in order to accurately predict future behaviours. Previous research has successfully demonstrated that ANN-based simulators and controllers can be developed concurrently during the ER process for a simple differentially-steered wheeled robot, while the current research has demonstrated the concurrent approach using a complex snake-like robot. The viability of the concurrent approach was demonstrated on a real-world snake-like robot by performing trajectory planning tasks. Influential factors related to the success of the concurrent approach were also studied by investigating the effects various parameter settings had on success. (C) 2016 E

关键词： evolutionary robotics Coevolution Simulator Artificial Neural Networks Snake robot

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Inference of Other's Minds with Limited Information in evolutionary robotics

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INTERNATIONAL JOURNAL OF SOCIAL robotics 2021年第4期13卷 661-676页

作者： Kim, Kyung-Joong Cho, Sung-Bae GIST Sch Integrated Technol Gwangju South Korea Yonsei Univ Dept Comp Sci Seoul South Korea

Theory of mind (ToM) is the ability to understand others' mental states (e.g., intentions). Studies on human ToM show that the way we understand others' mental states is very efficient, in the sense that observing only some portion of others' behaviors can lead to successful performance. Recently, ToM has gained interest in robotics to build robots that can engage in complex social interactions. Although it has been shown that robots can infer others' internal states, there has been limited focus on the data utilization of ToM mechanisms in robots. Here we show that robots can infer others' intentions based on limited information by selectively and flexibly using behavioral cues similar to humans. To test such data utilization, we impaired certain parts of an actor robot's behavioral information given to the observer, and compared the observer's performance under each impairment condition. We found that although the observer's performance was not perfect compared to when all information was available, it could infer the actor's mind to a degree if the goal-relevant information was intact. These results demonstrate that, similar to humans, robots can learn to infer others' mental states with limited information.

关键词： evolutionary robotics Estimation-exploration algorithm Neural network Physics-based simulation Information loss

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Abstraction, Sensory-Motor Coordination, and the Reality Gap in evolutionary robotics

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ARTIFICIAL LIFE 2017年第2期23卷 124-141页

作者： Scheper, Kirk Y. W. de Croon, Guido C. H. E. Delft Inst Technol Delft Netherlands Delft Univ Technol Fac Aerosp Engn NL-2629 HS Delft Netherlands

One of the major challenges of evolutionary robotics is to transfer robot controllers evolved in simulation to robots in the real world. In this article, we investigate abstraction of the sensory inputs and motor actions as a tool to tackle this problem. Abstraction in robots is simply the use of preprocessed sensory inputs and low-level closed-loop control systems that execute higher-level motor commands. To demonstrate the impact abstraction could have, we evolved two controllers with different levels of abstraction to solve a task of forming an asymmetric triangle with a homogeneous swarm of micro air vehicles. The results show that although both controllers can effectively complete the task in simulation, the controller with the lower level of abstraction is not effective on the real vehicle, due to the reality gap. The controller with the higher level of abstraction is, however, effective both in simulation and in reality, suggesting that abstraction can be a useful tool in making evolved behavior robust to the reality gap. Additionally, abstraction aided in reducing the computational complexity of the simulation environment, speeding up the optimization process. Preeminently, we show that the optimized behavior exploits the environment (in this case the identical behavior of the other robots) and performs input shaping to allow the vehicles to fly into and maintain the required formation, demonstrating clear sensory-motor coordination. This shows that the power of the genetic optimization to find complex correlations is not necessarily lost through abstraction as some have suggested.

关键词： Sensory-motor control evolutionary robotics reality gap micro air vehicle

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Friends reunited? evolutionary robotics and representational explanation

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ARTIFICIAL LIFE 2005年第1-2期11卷 215-231页

作者： Wheeler, M Univ Dundee Dept Philosophy Dundee DD1 4HN Scotland

robotics as practiced within the artificial life community is no longer the bitter enemy of representational explanation in the that it sometimes seemed to be in the heady revolutionary days of the 1990s. This rapprochement is, however, fragile, because the field of evolutionary robotics continues to pose two important challenges to the idea that real-time intelligent action must or should be explained by appeal to inner representations. The first of these challenges, the threat from nontrivial causal spread, occurs when extra-neural factors account for the kind of adaptive richness and flexibility normally associated with representation-based control. The second, the threat from continuous reciprocal causation, occurs when the causal contributions made by the systemic components collectively responsible for behavior generation are massively context-sensitive and variable over time. I argue that while the threat from nontrivial causal spread can be resisted, the threat from continuous reciprocal causation provides a stern test for our representational intuitions.

关键词： evolutionary robotics intelligent action modularity representation

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