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The Role of Morphological Variation in evolutionary robotics: Maximizing Performance and Robustness

evolutionary COMPUTATION 2024年第2期32卷 125-142页

作者： Carvalho, Jonata Tyska Nolfi, Stefano Fed Univ Santa Catarina UFSC Informat & Stat Dept Florianopolis Brazil Natl Res Council CNR Inst Cognit Sci & Technol ISTC Rome Italy

Exposing an evolutionary algorithm that is used to evolve robot controllers to variable conditions is necessary to obtain solutions which are robust and can cross the reality gap. However, we do not yet have methods for analyzing and understanding the impact of the varying morphological conditions which impact the evolutionary process, and therefore for choosing suitable variation ranges. By morphological conditions, we refer to the starting state of the robot, and to variations in its sensor readings during operation due to noise. In this paper, we introduce a method that permits us to measure the impact of these morphological variations and we analyze the relation between the amplitude of variations, the modality with which they are introduced, and the performance and robustness of evolving agents. Our results demonstrate that (i) the evolutionary algorithm can tolerate morphological variations which have a very high impact, (ii) variations affecting the actions of the agent are tolerated much better than variations affecting the initial state of the agent or of the environment, and (iii) improving the accuracy of the fitness measure through multiple evaluations is not always useful. Moreover, our results show that morphological variations permit generating solutions which perform better both in varying and non-varying conditions.

关键词： evolutionary robotics evolution strategies morphological variation

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Evolving cognitive scaffolding and environment adaptation: a new research direction for evolutionary robotics

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CONNECTION SCIENCE 2004年第4期16卷 339-350页

作者： Ziemke, T Bergfeldt, N Buason, G Susi, T Svensson, H Univ Skovde Sch Humanities & Informat S-54128 Skovde Sweden

Many researchers in embodied cognitive science and artificial intelligence, and evolutionary robotics in particular, emphasize the interaction of brain, body and environment as crucial to the emergence of intelligent, adaptive behaviour. Accordingly, the interaction between agent and environment, as well as the co-adaptation of artificial brains and bodies, has been the focus of much research in evolutionary robotics. Hence, there are plenty of studies of robotic agents/species adapting to a given environment. Many animals, on the other hand, in particular humans, to some extent can choose to adapt the environment to their own needs instead of adapting (only) themselves. That alternative has been studied relatively little in robot experiments. This paper, therefore, presents some simple initial simulation experiments, in a delayed response task setting, that illustrate how the evolution of environment adaptation can serve to provide cognitive scaffolding that reduces the requirements for individual agents. Furthermore, theoretical implications, open questions and future research directions for evolutionary robotics are discussed.

关键词： agent-environment interaction cognitive congeniality distributed cognition environment adaptation evolutionary robotics niche construction

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Extending the evolutionary robotics approach to flying machines: An application to MAV teams

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NEURAL NETWORKS 2009年第5-6期22卷 812-821页

作者： Ruini, Fabio Cangelosi, Angelo Univ Plymouth Sch Comp & Math Ctr Robot & Neural Syst Adapt Behav & Cognit Res Grp Plymouth PL4 8AA Devon England

The work presented in this article focuses on the use of embodied neural networks - developed through evolutionary robotics and Multi-Agent Systems methodologies - as autonomous distributed controllers for Micro-unmanned Aerial Vehicle (MAV) teams. The main aim of the research is to extend the range of domains that could be successfully tackled by the evolutionary robotics approach. The flying robots realm is an area that has not been yet thoroughly investigated by this discipline. This is due to the lack of an affordable and reliable robotic platform to use for carrying out experiments, and to the difficulty and the high computational load involved in experiments based upon a realistic software simulator for aircraft. We believe that the most recent improvements to the state of the art now permit the investigation of this domain. For demonstrating this point, two different evolutionary computer simulation models are presented in this article. The first model, which uses a simplified 2D test environment, has resulted in controllers evolved with the following capabilities: (1) navigation through unknown environments, (2) obstacle-avoidance, (3) tracking of a movable target, and (4) execution of cooperative and coordinated behaviors based on implicit communication strategies. In order to improve the robustness of these results and their potential use in real MAV teams, a more sophisticated 3D model is presented herein. The results obtained so far using the two models demonstrate the feasibility of the chosen approach for further research on the design of autonomous controllers for MAVs. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： MAV Neural network Multi-Agent System evolutionary robotics Distributed control

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Speciation in Behavioral Space for evolutionary robotics

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2011年第3-4期64卷 323-351页

作者： Trujillo, Leonardo Olague, Gustavo Lutton, Evelyne Fernandez de Vega, Francisco Dozal, Leon Clemente, Eddie Inst Tecnol Tijuana Tijuana BC Mexico Ctr Invest Cient & Educ Super Ensenada Div Fis Aplicada Dept Ciencias Comp Ensenada 22860 Baja California Mexico Univ Paris 11 AVIZ Team INRIA Saclay Ile De France F-91405 Orsay France Univ Extremadura Ctr Univ Merida Grp Evoluc Artificial Merida 06800 Venezuela Tecnol Estudios Super Ecatepec Ecatepec De Morelos Edo De Mexico Mexico

In evolutionary robotics, population-based evolutionary computation is used to design robot neurocontrollers that produce behaviors which allow the robot to fulfill a user-defined task. However, the standard approach is to use canonical evolutionary algorithms, where the search tends to make the evolving population converge towards a single behavioral solution, even if the high-level task could be accomplished by structurally different behaviors. In this work, we present an approach that preserves behavioral diversity within the population in order to produce a diverse set of structurally different behaviors that the robot can use. In order to achieve this, we employ the concept of speciation, where the population is dynamically subdivided into sub-groups, or species, each one characterized by a particular behavioral structure that all individuals within that species share. Speciation is achieved by describing each neurocontroller using a representations that we call a behavior signature, these are descriptors that characterize the traversed path of the robot within the environment. Behavior signatures are coded using character strings, this allows us to compare them using a string similarity measure, and three measures are tested. The proposed behavior-based speciation is compared with canonical evolution and a method that speciates based on network topology. Experimental tests were carried out using two robot tasks (navigation and homing behavior), several training environments, and two different robots (Khepera and Pioneer), both real and simulated. Results indicate that behavior-based speciation increases the diversity of the behaviors based on their structure, without sacrificing performance. Moreover, the evolved controllers exhibit good robustness when the robot is placed within environments that were not used during training. In conclusion, the speciation method presented in this work allows an evolutionary algorithm to produce several robot behaviors that a

关键词： evolutionary robotics Speciation Behavioral space

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Maze exploration behaviors using an integrated evolutionary robotics environment

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robotics AND AUTONOMOUS SYSTEMS 2004年第3期46卷 159-173页

作者： Nelson, AL Grant, E Galeotti, JM Rhody, S N Carolina State Univ Dept Elect & Comp Engn CRIM Raleigh NC 27695 USA

This paper presents results generated with a new evolutionary robotics (ER) simulation environment and its complementary real mobile robot colony research test-bed. Neural controllers producing mobile robot maze searching and exploration behaviors using binary tactile sensors as inputs were evolved in a simulated environment and subsequently transferred to and tested on real robots in a physical environment. There has been a considerable amount of proof-of-concept and demonstration research done in the field of ER control in recent years, most of which has focused on elementary behaviors such as object avoidance and homing. Artificial neural networks (ANN) are the most commonly used evolvable controller paradigm found in current ER literature. Much of the research reported to date has been restricted to the implementation of very simple behaviors using small ANN controllers. In order to move beyond the proof-of-concept stage our ER research was designed to train larger more complicated ANN controllers, and to implement those controllers on real robots quickly and efficiently. To achieve this a physical robot test-bed that includes a colony of eight real robots with advanced computing and communication abilities was designed and built. The real robot platform has been coupled to a simulation environment that facilitates the direct wireless transfer of evolved neural controllers from simulation to real robots (and vice versa). We believe that it is the simultaneous development of ER computing systems in both the simulated and the physical worlds that will produce advances in mobile robot colony research. Our simulation and training environment development focuses on the definition and training of our new class of ANNs, networks that include multiple hidden layers, and time-delayed and recurrent connections. Our physical mobile robot design focuses on maximizing computing and communications power while minimizing robot size, weight, and energy usage. The simulation and

关键词： evolutionary robotics Behavioral robotics evolutionary neural networks Distributed robotics robot colonies

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Combining simulation and reality in evolutionary robotics

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2007年第1期50卷 19-39页

作者： Zagal, Juan Cristobal Ruiz-del-Solar, Javier Univ Chile Dept Elect Engn Santiago 6513027 Chile

evolutionary robotics (ER) is a promising methodology, intended for the autonomous development of robots, in which their behaviors are obtained as a consequence of the structural coupling between robot and environment. It is essential that there be a great amount of interaction to generate complex behaviors. Thus, nowadays, it is common to use simulation to speed up the learning process;however simulations are achieved from arbitrary off-line designs, rather than from the result of embodied cognitive processes. According to the reality gap problem, controllers evolved in simulation usually do not allow the same behavior to arise once transferred to the real robot. Some preliminary approaches for combining simulation and reality exist in the ER literature;nonetheless, there is no satisfactory solution available. In this work we discuss recent advances in neuroscience as a motivation for the use of environmentally adapted simulations, which can be achieved through the co-evolution of robot behavior and simulator. We present an algorithm in which only the differences between the behavior fitness obtained in reality versus that obtained in simulations are used as feedback for adapting a simulation. The proposed algorithm is experimentally validated by showing the successful development and continuous transference to reality of two complex low-level behaviors with Sony AIBO1 robots: gait optimization and ball-kicking behavior.

关键词： AIBO robots evolutionary robotics mobile robotics organismically inspired robotics RoboCup simulation of solid dynamics

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Fitness functions in evolutionary robotics: A survey and analysis

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robotics AND AUTONOMOUS SYSTEMS 2009年第4期57卷 345-370页

作者： Nelson, Andrew L. Barlow, Gregory J. Doitsidis, Lefteris Androtics LLC Tucson AZ 85733 USA Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA Tech Univ Crete Dept Prod Engn & Management Intelligent Syst & Robot Lab Hania 73132 Greece

This paper Surveys Fitness functions used in the field of evolutionary robotics (ER). evolutionary robotics is a field of research that applies artificial evolution to generate control systems for autonomous robots. During evolution, robots attempt to perform a given task in a given environment. The controllers in the better performing robots are selected, altered and propagated to perform the task again in an iterative process that mimics some aspects of natural evolution. A key component of this process - one might argue, the key component - is the measurement of Fitness in the evolving controllers. ER is one of a host of machine learning methods that rely oil interaction with, and feedback from, a complex dynamic environment to drive synthesis of controllers for autonomous agents. These methods have the potential to lead to the development of robots that can adapt to uncharacterized environments and which may be able to perform tasks that human designers do not completely understand. In order to achieve this, issues regarding fitness evaluation must be addressed. In this paper we survey current ER research and focus oil work that involved real robots. The Surveyed research is organized according to the degree of a priori knowledge used to formulate the various Fitness functions employed during evolution. The underlying motivation for this is to identify methods that allow the development of the greatest degree of novel control, while requiring the minimum amount of a priori task knowledge from the designer. (c) 2008 Elsevier B.V. All rights reserved.

关键词： evolutionary robotics Fitness functions Genetic algorithms Autonomous learning robots Artificial life

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The Transferability Approach: Crossing the Reality Gap in evolutionary robotics

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2013年第1期17卷 122-145页

作者： Koos, Sylvain Mouret, Jean-Baptiste Doncieux, Stephane Univ Paris 06 Inst Intelligent Syst F-75005 Paris France Univ Paris 06 Robot Lab F-75005 Paris France

The reality gap, which often makes controllers evolved in simulation inefficient once transferred onto the physical robot, remains a critical issue in evolutionary robotics (ER). We hypothesize that this gap highlights a conflict between the efficiency of the solutions in simulation and their transferability from simulation to reality: the most efficient solutions in simulation often exploit badly modeled phenomena to achieve high fitness values with unrealistic behaviors. This hypothesis leads to the transferability approach, a multiobjective formulation of ER in which two main objectives are optimized via a Pareto-based multiobjective evolutionary algorithm: 1) the fitness;and 2) the transferability, estimated by a simulation-to-reality (STR) disparity measure. To evaluate this second objective, a surrogate model of the exact STR disparity is built during the optimization. This transferability approach has been compared to two reality-based optimization methods, a noise-based approach inspired from Jakobi's minimal simulation methodology and a local search approach. It has been validated on two robotic applications: 1) a navigation task with an e-puck robot;and 2) a walking task with a 8-DOF quadrupedal robot. For both experimental setups, our approach successfully finds efficient and well-transferable controllers only with about ten experiments on the physical robot.

关键词： evolutionary robotics reality gap transferability approach

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Encouraging Behavioral Diversity in evolutionary robotics: An Empirical Study

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evolutionary COMPUTATION 2012年第1期20卷 91-133页

作者： Mouret, J. -B. Doncieux, S. Univ Paris 06 CNRS UMR 7222 ISIR F-75252 Paris 05 France

evolutionary robotics (ER) aims at automatically designing robots or controllers of robots without having to describe their inner workings. To reach this goal, ER researchers primarily employ phenotypes that can lead to an infinite number of robot behaviors and fitness functions that only reward the achievement of the task and not how to achieve it. These choices make ER particularly prone to premature convergence. To tackle this problem, several papers recently proposed to explicitly encourage the diversity of the robot behaviors, rather than the diversity of the genotypes as in classic evolutionary optimization. Such an approach avoids the need to compute distances between structures and the pitfalls of the noninjectivity of the phenotype/behavior relation;however, it also introduces new questions: how to compare behavior? should this comparison be task specific? and what is the best way to encourage diversity in this context? In this paper, we review the main published approaches to behavioral diversity and benchmark them in a common framework. We compare each approach on three different tasks and two different genotypes. The results show that fostering behavioral diversity substantially improves the evolutionary process in the investigated experiments, regardless of genotype or task. Among the benchmarked approaches, multi-objective methods were the most efficient and the generic, Hamming-based, behavioral distance was at least as efficient as task specific behavioral metrics.

关键词： evolutionary robotics diversity multi-objective evolutionary algorithm neural networks

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Behavior Trees for evolutionary robotics

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ARTIFICIAL LIFE 2016年第1期22卷 23-48页

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

evolutionary robotics allows robots with limited sensors and processing to tackle complex tasks by means of sensory-motor coordination. In this article we show the first application of the Behavior Tree framework on a real robotic platform using the evolutionary robotics methodology. This framework is used to improve the intelligibility of the emergent robotic behavior over that of the traditional neural network formulation. As a result, the behavior is easier to comprehend and manually adapt when crossing the reality gap from simulation to reality. This functionality is shown by performing real-world flight tests with the 20-g DelFly Explorer flapping wing micro air vehicle equipped with a 4-g onboard stereo vision system. The experiments show that the DelFly can fully autonomously search for and fly through a window with only its onboard sensors and processing. The success rate of the optimized behavior in simulation is 88%, and the corresponding real-world performance is 54% after user adaptation. Although this leaves room for improvement, it is higher than the 46% success rate from a tuned user-defined controller.

关键词： Behavior tree evolutionary robotics reality gap micro air vehicle

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