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Mechatronics and robotics Engineering (ICMRE), International Conference on

作者： Oussama Jaffal Zahra Taghizadeh Melike Cezayirlioglu Weipeng Li Xuan Wu Kamilo Melo KM-RoBoTa Renens Switzerland Robotics Laboratory China Nanhu Academy of Electronics and Information Technology Jiaxing Zhejiang China

ISBN: (数字)9798331509293

ISBN: (纸本)9798331509309

Efficient and robust design of legged robots for diverse terrains remains a critical focus in robotics research. To streamline this process we present varyMorph, an open-source tool that programmatically generates diverse legged robot models in both URDF and USDA formats. By randomizing properties such as leg count, morphology, body dimensions, and mass distribution, varyMorph creates hundreds or thousands of valid robot designs in few minutes. Users can seamlessly integrate these models into popular simulators (e.g., Isaac Sim/Lab, MuJoCo, Isaac Gym, RaiSim), accelerating large-scale experimentation in evolutionary robotics and reinforcement learning. Our results show near-linear scalability with respect to complexity and batch size, achieving rapid generation on both high-performance and standard desktop hardware. This high-throughput morphological exploration facilitates data-driven optimization and supports novel research on locomotion control, sim - to- real transfer, and co-evolutionary algorithms. We envision varyMorph as a complementary resource for grammar-based, latent-space, and gradient-driven design frameworks, reducing the barrier to discovering innovative and robust robot morpholoaies.

关键词： Legged locomotion evolutionary robotics Mechatronics Scalability Morphology Reinforcement learning Hardware Robots Standards Optimization

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Generational Replacement and Learning for High-Performing and Diverse Populations in Evolvable Robots

Generational Replacement and Learning for High-Performing an...

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Computational Intelligence in Artificial Life and Cooperative Intelligent Systems (ALIFE-CIS), IEEE Symposium on

作者： K. Ege de Bruin Kyrre Glette Kai Olav Ellefsen Department of Informatics University of Oslo Oslo Norway RITMO Department of Informatics University of Oslo Oslo Norway

ISBN: (数字)9798331508371

ISBN: (纸本)9798331508388

evolutionary robotics offers the possibility to design robots to solve a specific task automatically by optimizing their morphology and control together. However, this co-optimization of body and control is challenging, because controllers need some time to adapt to the evolving morphology - which may make it difficult for new and promising designs to enter the evolving population. A solution to this is to add intra-life learning, defined as an additional controller optimization loop, to each individual in the evolving population. A related problem is the lack of diversity often seen in evolving populations as evolution narrows the search down to a few promising designs too quickly. This problem can be mitigated by implementing full generational replacement, where offspring robots replace the whole population. This solution for increasing diversity usually comes at the cost of lower performance compared to using elitism. In this work, we show that combining such generational replacement with intra-life learning can increase diversity while retaining performance. We also highlight the importance of performance metrics when studying learning in morphologically evolving robots, showing that evaluating according to function evaluations versus according to generations of evolution can give different conclusions.

关键词： evolutionary robotics Costs Diversity reception Morphology Robot sensing systems Search problems Encoding Intelligent systems Robots Optimization

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Embodied Evolution: Distributing an evolutionary algorithm in a population of robots

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robotics AND AUTONOMOUS SYSTEMS 2002年第1期39卷 1-18页

作者： Watson, RA Ficici, SG Pollack, JB Brandeis Univ Volen Natl Ctr Complex Syst Dynam & Evolutionary Machine Org Waltham MA 02454 USA

We introduce Embodied Evolution (EE) as a new methodology for evolutionary robotics (ER). EE uses a population of physical robots that autonomously reproduce with one another while situated in their task environment. This constitutes a fully distributed evolutionary algorithm embodied in physical robots. Several issues identified by researchers in the evolutionary robotics community as problematic for the development of ER are alleviated by the use of a large number of robots being evaluated in parallel. Particularly, EE avoids the pitfalls of the simulate-and-transfer method and allows the speed-up of evaluation time by utilizing parallelism. The more novel features of EE are that the evolutionary algorithm is entirely decentralized, which makes it inherently scalable to large numbers of robots, and that it uses many robots in a shared task environment, which makes it an interesting platform for future work in collective robotics and Artificial Life. We have built a population of eight robots and successfully implemented the first example of Embodied Evolution by designing a fully decentralized, asynchronous evolutionary algorithm. Controllers evolved by EE outperform a hand-designed controller in a simple application. We introduce our approach and its motivations, detail our implementation and initial results, and discuss the advantages and limitations of EE. (C) 2002 Elsevier Science B.V. All lights reserved.

关键词： evolutionary robotics artificial life evolutionary algorithms distributed learning collective robotics

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evolutionary robots with on-line self-organization and behavioral fitness

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NEURAL NETWORKS 2000年第4-5期13卷 431-443页

作者： Floreano, D Urzelai, J Swiss Fed Inst Technol Inst Robot Syst CH-1015 Lausanne Switzerland Swiss Fed Inst Technol Lab Microcomp CH-1015 Lausanne Switzerland

We address two issues in evolutionary robotics, namely the genetic encoding and the performance criterion, also known as the fitness function. For the first aspect, we suggest to encode mechanisms for parameter self-organization, instead of the parameters themselves as in conventional approaches. We argue that the suggested encoding generates systems that can solve more complex tasks and are more robust to unpredictable sources of change. We support our arguments with a set of experiments on evolutionary neural controllers for physical robots and compare them to conventional encoding. In addition, we show that when also the genetic encoding is left free to evolve, artificial evolution will select to exploit mechanisms of self-organization. For the second aspect, we shall discuss the role of the performance criterion, also known as fitness function, and suggest Fitness Space as a framework to conceive fitness functions in evolutionary robotics. Fitness Space can be used as a guide to design fitness functions as well as to compare different experiments in evolutionary robotics. (C) 2000 Elsevier Science Ltd. All rights reserved.

关键词： evolutionary robotics fitness space adaptive behavior neural evolution and learning

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evolutionary design and behavior analysis of neuromodulatory neural networks for mobile robots control

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APPLIED SOFT COMPUTING 2007年第1期7卷 189-202页

作者： Kondo, Toshiyuki Tokyo Inst Technol G3 50 Interdisciplinary Grad Sch Sci & Engn Dept Computat Intelligence & Syst Sci Midori Ku Yokohama Kanagawa 2268502 Japan

evolutionary robotics (ER) is one of promising approaches to design robot controllers which essentially have complicated and/or complex properties. In most ER research, the sensory - motor mappings of robots are represented as artificial neural networks, and their connection weights ( and sometimes the structure of the networks) can be optimized in the parameter spaces by using evolutionary computation. However, generally, the evolved neural controllers could be fragile in unexperienced environments, especially in real worlds, because the evolutionary optimization processes would be executed in idealized simulators. This is known as the gap problem between the simulated and real worlds. To overcome this, the author focused on evolving an on-line learning ability instead of weight parameters in a simulated environment. According to recent biological findings, actually, the kinds of on-line adaptation abilities can be found in real nervous systems of insects and crustaceans, and it is also known that a variety of neuromodulators (NMs) play crucial roles to regulate the network characteristics (i.e. activating/ blocking/changing of synaptic connections). Based on this, a neuromodulatory neural network model was proposed and it was utilized as a mobile robot controller. In the paper, the detail behavior analysis of the evolved neuromodulatory neural network is also discussed. (C) 2005 Elsevier B.V. All rights reserved.

关键词： evolutionary robotics neuromodulation neural networks mobile robot control

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The evolutionary Locomotion of Tripedal and Quadrupedal Biomorphic Robots

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2013年第4期29卷 681-693页

作者： Qiu, Guo-Yuan Wu, Shih-Hung Chaoyang Univ Technol Dept Comp Sci & Informat Engn Taichung 413 Taiwan

evolutionary robots can achieve certain goals via evolutionary algorithms without specifying all the detailed actions. The robot interacts with the environment and receives natural feedback as the fitness of its goal. In this paper, we study two biomorphic robots and observe how the behavior can evolve with least computation resources and test the possibility of self-adjustment on an unexpected motor failure of one robot leg. The feedback is collected via a supersonic sensor and the evolutionary algorithm is a modified low cost genetic algorithm. In order to minimize the cost of evolution, we control the number of the population of each generation to a very low number. We find that the biomorphic robots can move faster with the motion sequence generated by the evolutionary algorithm than with the motion sequence assigned by human programmers. The structures of robots, in this case, and the number of legs might affect the speed of convergence. Our experiment result shows that the robot can adjust itself with this online evolving approach to deal with an unexpected motor fault. That is, evolutionary robot can overcome an unexpected situation without human intervention.

关键词： evolutionary robotics biomorphic robot evolutionary algorithm adaptive robot tripedal robot

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A Developmental evolutionary Learning Framework for Robotic Chinese Stroke Writing

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 2022年第3期14卷 1155-1169页

作者： Wu, Ruiqi Chao, Fei Zhou, Changle Huang, Yuxuan Yang, Longzhi Lin, Chih-Min Chang, Xiang Shen, Qiang Shang, Changjing Henan Univ Technol Sch Artificial Intelligence & Big Data Dept Artificial Intelligence Zhengzhou 471023 Peoples R China Xiamen Univ Sch Informat Dept Artificial Intelligence Xiamen 361005 Peoples R China Aberystwyth Univ Inst Math Phys & Comp Sci Dept Comp Sci Aberystwyth SY23 3DB Dyfed Wales Software Dev Ctr Bank Commun Shanghai 518028 Peoples R China Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST Tyne & Wear England Yuan Ze Univ Dept Elect Engn Taoyuan 32003 Taiwan

The ability of robots to write Chinese strokes, which is recognized as a sophisticated task, involves complicated kinematic control algorithms. The conventional approaches for robotic writing of Chinese strokes often suffer from limited font generation methods, which limits the ability of robots to perform high-quality writing. This article instead proposes a developmental evolutionary learning framework that enables a robot to learn to write fundamental Chinese strokes. The framework first considers the learning process of robotic writing as an evolutionary easy-to-difficult procedure. Then, a developmental learning mechanism called "Lift-constraint, act and saturate" that stems from developmental robotics is used to determine how the robot learns tasks ranging from simple to difficult by building on the learning results from the easy tasks. The developmental constraints, which include altitude adjustments, number of mutation points, and stroke trajectory points, determine the learning complexity of robot writing. The developmental algorithm divides the evolutionary procedure into three developmental learning stages. In each stage, the stroke trajectory points gradually increase, while the number of mutation points and adjustment altitudes gradually decrease, allowing the learning difficulties involved in these three stages to be categorized as easy, medium, and difficult. Our robot starts with an easy learning task and then gradually progresses to the medium and difficult tasks. Under various developmental constraint setups in each stage, the robot applies an evolutionary algorithm to handle the basic shapes of the Chinese strokes and eventually acquires the ability to write with good quality. The experimental results demonstrate that the proposed framework allows a calligraphic robot to gradually learn to write five fundamental Chinese strokes and also reveals a developmental pattern similar to that of humans. Compared to an evolutionary algorithm without the dev

关键词： Robots Robot kinematics Writing Trajectory Robot sensing systems evolutionary computation Task analysis Developmental learning evolutionary algorithm evolutionary robotics robotic writing

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Behavioral control through evolutionary neurocontrollers for autonomous mobile robot navigation

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robotics AND AUTONOMOUS SYSTEMS 2009年第4期57卷 411-419页

作者： Fernandez-Leon, Jose A. Acosta, Gerardo G. Mayosky, Miguel A. Univ Sussex Ctr Computat Neurosci & Robot Brighton E Sussex England UNCPBA Fac Engn Grp INTELYMEC Buenos Aires DF Argentina UNLP Fac Engn LEICI Lab Buenos Aires DF Argentina Consejo Nacl Invest Cient & Tecn Natl Council Sci & Technol Res RA-1033 Buenos Aires DF Argentina Comis Invest Cient Gob Prov Buenos Aires Buenos Aires DF Argentina

This paper deals with the study of scaling up behaviors in evolutive robotics (ER). Complex behaviors were obtained from simple ones. Each behavior is Supported by an artificial neural network (ANN)-based controller or neurocontroller. Hence, a method for the generation of a hierarchy of neurocontrollers, resorting to the paradigm of Layered Evolution (LE), is developed and verified experimentally through computer simulations and tests in a Khepera (R) micro-robot. Several behavioral modules are initially evolved using specialized neurocontrollers based oil different ANN paradigms. The results show that simple behaviors coordination through LE is a feasible strategy that gives rise to emergent complex behaviors. These complex behaviors can then solve real-world problems efficiently. From a pure evolutionary perspective, however, the methodology presented is too much dependent on user's prior knowledge about the problem to solve and also that evolution take place in a rigid, prescribed framework. Mobile robot's navigation in an unknown environment is used as a test bed for the proposed scaling strategies. (c) 2008 Elsevier B.V. All rights reserved.

关键词： evolutionary robotics Adaptive systems Behavior emergence

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Robot Mission Planning using Co-evolutionary Optimization

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ROBOTICA 2020年第3期38卷 512-530页

作者： Rahul, Kala Indian Inst Informat Technol Robot & Machine Intelligence Lab Allahabad Uttar Pradesh India

Mission planning is a complex motion planning problem specified by using Temporal Logic constituting of Boolean and temporal operators, typically solved by model verification algorithms with an exponential complexity. The paper proposes co-evolutionary optimization thus building an iterative solution to the problem. The language for mission specification is generic enough to represent everyday missions, while specific enough to design heuristics. The mission is broken into components which cooperate with each other. The experiments confirm that the robot is able to outperform the search, evolutionary and model verification techniques. The results are demonstrated by using a Pioneer LX robot.

关键词： evolutionary robotics Robot motion planning Mission planning Cooperative coevolution Genetic algorithm

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Generating high-speed dynamic running gaits in a quadruped robot using an evolutionary search

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 2004年第4期34卷 1685-1696页

作者： Krasny, DP Orin, DE Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA

Over the past several decades, there has been a considerable interest in investigating high-speed dynamic gaits for legged robots. While much research has been published, both in the biomechanics and engineering fields regarding the analysis of these gaits, no single study has adequately characterized the dynamics of high-speed running as can be achieved in a realistic, yet simple, robotic system. The goal of this paper is to find the most energy-efficient, natural, and unconstrained gallop that can be achieved using a simulated quadrupedal robot with articulated legs, asymmetric mass distribution, and compliant legs. For comparison purposes, we also implement the bound and canter. The model used here is planar, although we will show that it captures much of the predominant dynamic characteristics observed in animals. While it is not our goal to prove anything about biological locomotion, the dynamic similarities between the gaits we produce and those found in animals does indicate a similar underlying dynamic mechanism. Thus, we will show that achieving natural, efficient high-speed locomotion is possible even with a fairly simple robotic system. To generate the high-speed gaits, we use an efficient evolutionary algorithm called set-based stochastic optimization. This algorithm rinds open-loop control parameters to generate periodic trajectories for the body. Several alternative methods are tested to generate periodic trajectories for the legs. The combined solutions found by the evolutionary search and the periodic-leg methods, over a range of speeds up to 10.0 m/s, reveal "biological" characteristics that are emergent properties of the underlying gaits.

关键词： biomechanics evolutionary robotics gallop legged locomotion quadruped running gaits

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