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Controlling maximum evaluation duration in on-line and on-board evolutionary robotics On-the-fly control of maximum evaluation duration in on-line and on-board evolutionary robotics in highly dynamic and uncertain environments

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EVOLVING SYSTEMS 2014年第4期5卷 275-286页

作者： Arif, Atta-Ul-Qayyum Nedev, D. G. Haasdijk, Evert Vrije Univ Amsterdam Amsterdam Netherlands MonetDB Solut Amsterdam Netherlands

On-line evolution of robot controllers allows robots to adapt while they perform their proper tasks. In our investigations, robots contain their own self-sufficient evolutionary algorithm (known as the encapsulated approach) where individual solutions are evaluated by means of a time sharing scheme: an individual controller is given the run of the robot for some amount of time and fitness corresponds to the robot's task performance during that period. In this paper, we propose and provide a detailed analysis of two on-the-fly control schemes to set the evaluation time in highly dynamic scenarios with completely different tasks. One scheme, called the roulette-wheel selection scheme, stochastically selects evaluation time from promising intervals similar to multi-armed bandit schemes. The other scheme, named Heuristic-Rule (H-Rule), tweaks the evaluation time using specific heuristics. Our experiments show that H-Rule gives stable performance in different scenarios and can serve as a viable alternative to pre-selected optimal evaluation time.

关键词： On-line evolution Parameter control evolutionary robotics Racing On-board evolution Heuristics

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Beyond black-box optimization: a review of selective pressures for evolutionary robotics

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evolutionary INTELLIGENCE 2014年第2期7卷 71-93页

作者： Doncieux, Stephane Mouret, Jean-Baptiste UPMC Univ Paris 06 Sorbonne Univ UMR 7222 ISIR F-75005 Paris France CNRS UMR 7222 ISIR F-75005 Paris France

evolutionary robotics (ER) is often viewed as the application of a family of black-box optimization algorithms-evolutionary algorithms-to the design of robots, or parts of robots. When considering ER as black-box optimization, the selective pressure is mainly driven by a user-defined, black-box fitness function, and a domain-independent selection procedure. However, most ER experiments face similar challenges in similar setups: the selective pressure, and, in particular, the fitness function, is not a pure user-defined black box. The present review shows that, because ER experiments share common features, selective pressures for ER are a subject of research on their own. The literature has been split into two categories: goal refiners, aimed at changing the definition of a good solution, and process helpers, designed to help the search process. Two sub-categories are further considered: task-specific approaches, which require knowledge on how to solve the task and task-agnostic ones, which do not need it. Besides highlighting the diversity of the approaches and their respective goals, the present review shows that many task-agnostic process helpers have been proposed during the last years, thus bringing us closer to the goal of a fully automated robot behavior design process.

关键词： evolutionary robotics Selective pressures Goal refiner Process helper Task-specific Task-agnostic

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Investigating intertemporal choice through experimental evolutionary robotics

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BEHAVIOURAL PROCESSES 2015年 115卷 1-18页

作者： Paglieri, Fabio Parisi, Domenico Patacchiola, Massimiliano Petrosino, Giancarlo ISTC CNR Goal Oriented Agents Lab Rome Italy ISTC CNR Lab Artificial Life & Robot LARAL Rome Italy Univ Plymouth Sch Comp & Math Ctr Robot & Neural Syst Plymouth PL4 8AA Devon England

In intertemporal choices, subjects face a trade-off between value and delay: achieving the most valuable outcome requires a longer time, whereas the immediately available option is objectively poorer. Intertemporal choices are ubiquitous, and comparative studies reveal commonalities and differences across species: all species devalue future rewards as a function of delay (delay aversion), yet there is a lot of inter-specific variance in how rapidly such devaluation occurs. These differences are often interpreted in terms of ecological rationality, as depending on environmental factors (e.g., feeding ecology) and the physiological and morphological constraints of different species (e.g., metabolic rate). evolutionary hypotheses, however, are hard to verify in vivo, since it is difficult to observe precisely enough real environments, not to mention ancestral ones. In this paper, we discuss the viability of an approach based on evolutionary robotics: in Study 1, we evolve robots without a metabolism in five different ecologies;in Study 2, we evolve metabolic robots (i.e., robots that consume energy over time) in three different ecologies. The intertemporal choices of the robots are analyzed both in their ecology and under laboratory conditions. Results confirm the generality of delay aversion and the usefulness of studying intertemporal choice through experimental evolutionary robotics. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Intertemporal choice evolutionary robotics Ecological rationality Delay discounting Spatial discounting Preferences

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The Effect of Fitness Function Design on Performance in evolutionary robotics: The Influence of a Priori Knowledge 15

The Effect of Fitness Function Design on Performance in Evol...

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17th Genetic and evolutionary Computation Conference (GECCO)

作者： Soorati, Mohammad Divband Hamann, Heiko Univ Paderborn Dept Comp Sci D-33102 Paderborn Germany

ISBN: (纸本)9781450334723

Fitness function design is known to be a critical feature of the evolutionary-robotics approach. Potentially, the complexity of evolving a successful controller for a given task can be reduced by integrating a priori knowledge into the fitness function which complicates the comparability of studies in evolutionary robotics. Still, there are only few publications that study the actual effects of different fitness functions on the robot's performance. In this paper, we follow the fitness function classification of Nelson et al. (2009) and investigate a selection of four classes of fitness functions that require different degrees of a priori knowledge. The robot controllers are evolved in simulation using NEAT and we investigate different tasks including obstacle avoidance and (periodic) goal homing. The best evolved controllers were then post-evaluated by examining their potential for adaptation, determining their convergence rates, and using cross-comparisons based on the different fitness function classes. The results confirm that the integration of more a priori knowledge can simplify a task and show that more attention should be paid to fitness function classes when comparing different studies.

关键词： evolutionary robotics fitness function design

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An Update Method of Computer Simulation for evolutionary robotics

An Update Method of Computer Simulation for Evolutionary Rob...

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

作者： Katada, Yoshiaki Ohkura, Kazuhiro Setsunan Univ Fac Engn Neyagawa Osaka Japan Kobe Univ Fac Engn Kobe Hyogo Japan

ISBN: (纸本)9781586035952

One of advantages of evolutionary robotics over other approaches is its parallel population search. However, it generally takes an unrealistically long time to evaluate all candidate solutions by using a real robot. Thus, a technique of computer simulation is considered to be one of the most important topics in evolutionary robotics. Although it is quite difficult to provide a precise computer simulation model of a physical experiment, simulated robot/environment interaction dynamics must be synchronously consistent with the physical one during its evolution for reducing the gap between the simulated and physical one. In this paper, in order to overcome this problem, we propose an update method of simulated robot/environment interaction dynamics based on a statistical test during evolution, and then investigate the consistency between the performances of a robot in the simulated and physical environment. A series of experiments with a mobile robot illustrate the effectiveness of the proposed method in practice.

关键词： Simulation Real robot evolutionary robotics Embodied cognitive science

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Behavioral diversity measures for evolutionary robotics

Behavioral diversity measures for Evolutionary Robotics

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2010 IEEE World Congress on Computational Intelligence

作者： Doncieux, Stephane Mouret, Jean-Baptiste Univ Paris 06 ISIR CNRS UMR 7222 F-75005 Paris France

ISBN: (纸本)9781424481262

In evolutionary robotics (ER), explicitly rewarding for behavioral diversity recently revealed to generate efficient results without recourse to complex fitness functions. The principle of such approaches is to explicitly encourage diversity in the robot behavior space instead of in the space of genotypes (the space explored by the evolutionary algorithm) or the space of phenotypes (the space of robot controllers and morphologies). To implement such approaches, a similarity between behaviors needs to be evaluated but, up to now, used similarity measures are problem-specific. The goal of this work is to explore generic behavioral similarity measures that only rely on sensori-motor values. With such a measure, we managed to evolve the topology and the parameters of neuro-controllers that make a simulated robot go towards a ball, take it, find a basket, put the ball into the basket, perform a half-turn, search and take another ball, put it into the basket, etc. In this experiment, two objectives were simultaneously optimized with NSGA-II: the number of collected balls and the generic behavioral diversity objective. Several generic behavioral measures are compared. To confirm the interpretation of behavioral diversity objective and in an attempt to characterize behavioral similarity measures, they are also compared to human-made behavioral similarity evaluations. They reveal to classify behaviors globally as humans did, but with no clear correlation between the closeness to human classification and the efficiency within an evolutionary run.

关键词： NSGA-II evolutionary algorithm evolutionary robotics generic behavioral diversity measures generic behavioral similarity measures genetic algorithms human-made behavioral similarity evaluations neurocontrollers robot behavior space robot controllers robot morphologies robots sensori-motor values

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Using Standard Components in evolutionary robotics to Produce an Inexpensive Robot Arm 7th

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7th World Congress on Nature and Biologically Inspired Computing (NaBIC)

作者： Louwrens, Michael W. du Plessis, Mathys C. Greyling, Jean H. Nelson Mandela Meteropolitan Univ Univ Way ZA-6031 Port Elizabeth South Africa

ISBN: (纸本)9783319274003;9783319273990

evolutionary robotics (ER) makes use of evolutionary algorithms to evolve controllers and morphologies of robots. Despite successful demonstrations in laboratory experiments, ER has not been widely adopted by industry as means of robot design. A possible reason for this is that current ER approaches ignore issues that are important when designing robots for practical use. For example, the availability and cost of components used for robot construction should be considered. A robot designed by the ER process may require specialised custom components to be built to support the physical functioning of the design, if the components selected by an ER process are not widely available. Alternatively, the ER designed robot may be too expensive to be constructed. This paper demonstrates that standard off-the-shelf components can be used by the ER process to design a robot. A robot arm is used as a sample problem, which is successfully optimised to use components from a fixed list while minimising cost.

关键词： evolutionary robotics evolutionary computing Standard components

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Supplanting Neural Networks with ODEs in evolutionary robotics

Supplanting Neural Networks with ODEs in Evolutionary Roboti...

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8th International Conference of Simulated Evolution and Learning

作者： Grouchy, Paul D'Eleuterio, Gabriele M. T. Univ Toronto Inst Aerosp Studies Toronto ON Canada

ISBN: (纸本)9783642172977

A new approach to evolutionary robotics is presented. Neural networks are abstracted and supplanted by a system of ordinary differential equations that govern the changes in controller outputs. The equations are evolved as trees using an evolutionary algorithm based on symbolic regression in genetic programming. Initial proof-of-concept experiments are performed using a simulated two-wheeled robot that must drive a straight line while wheel response properties vary. Evolved controllers demonstrate the ability to learn and adapt;to a changing environment, as well as the ability to generalize and perform well in novel situations.

关键词： evolutionary robotics Genetic Programming evolutionary Algorithms Genetic Algorithms evolutionary Computing Learning

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Robotic Snake Simulation using Ensembles of Artificial Neural Networks in evolutionary robotics 18

Robotic Snake Simulation using Ensembles of Artificial Neura...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Woodford, Grant W. du Plessis, Mathys C. Nelson Mandela Univ Dept Comp Sci Port Elizabeth South Africa

ISBN: (纸本)9781450356183

The evolutionary robotics process requires the evaluation of large numbers of robot controllers in order to determine their relative fitnesses. The evaluation of many controllers is typically performed in simulation instead of real-world hardware in order to speed up the evolutionary process and avoid damage to robot hardware. Physics-based simulators are traditionally used in robotics for evaluating controllers. Effective traditional simulators may require a high level of accuracy and their creation requires specialised knowledge of the dynamics of the robotic system. Alternatively, Artificial Neural Network based simulators are relatively simple to construct, are highly accurate, efficient and assume little specialised knowledge of the dynamics involved. This paper proposes a novel controller evolution method that integrates uncertainty information from an ensemble of Artificial Neural Network based simulators. By estimating the accuracy of simulator predictions during the controller evolution process, robot behaviours that are not accurately simulated are likely to be avoided. A performance comparison of a single versus ensembles configuration of Artificial Neural Network based simulators is investigated. The ensemble approach developed in thiswork has so far outperformed all prior controller evolution methods investigated for the presented problem.

关键词： ACM proceedings evolutionary robotics Simulation Artificial Neural Networks

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Noise and the reality gap: The use of simulation in evolutionary robotics 3rd

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3rd European Conference on Artificial Life

作者： Jakobi, N Husbands, P Harvey, I School of Cognitive and Computing Sciences University of Sussex Brighton BN1 9QH United Kingdom

ISBN: (纸本)3540594965

The pitfalls of naive robot simulations have been recognised for areas such as evolutionary robotics. It has been suggested that carefully validated simulations with a proper treatment of noise may overcome these problems. This paper reports the results of experiments intended to test some of these claims. A simulation was constructed of a two-wheeled Khepera robot with IR and ambient light sensors. This included detailed mathematical models of the robot-environment interaction dynamics with empirically determined parameters. Artificial evolution was used to develop recurrent dynamical network controllers for the simulated robot, for obstacle-avoidance and light-seeking tasks, using different levels of noise in the simulation. The evolved controllers were down-loaded onto the real robot and the correspondence between behaviour in simulation and in reality was tested. The level of correspondence varied according to how much noise was used in the simulation, with very good results achieved when realistic quantities were applied. It has been demonstrated that it is possible to develop successful robot controllers in simulation that generate almost identical behaviours in reality, at least for a particular class of robot-environment interaction dynamics.

关键词： evolutionary robotics noise high fidelity simulations artificial evolution

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