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2021 Conference on Artificial Life, ALIFE 2021

作者： Zahadat, Payam IT University of Copenhagen Denmark

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Evolved Transistor Array Robot Controllers

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evolutionary COMPUTATION 2020年第4期28卷 677-708页

作者： Garvie, Michael Flascher, Ittai Philippides, Andrew Thompson, Adrian Husbands, Phil Univ Sussex Dept Informat Brighton BN1 9QJ E Sussex England Binderr Haifa Israel

For the first time, a field programmable transistor array (FPTA) was used to evolve robot control circuits directly in analog hardware. Controllers were successfully incrementally evolved for a physical robot engaged in a series of visually guided behaviours, including finding a target in a complex environment where the goal was hidden from most locations. Circuits for recognising spoken commands were also evolved and these were used in conjunction with the controllers to enable voice control of the robot, triggering behavioural switching. Poor quality visual sensors were deliberately used to test the ability of evolved analog circuits to deal with noisy uncertain data in realtime. Visual features were coevolved with the controllers to automatically achieve dimensionality reduction and feature extraction and selection in an integrated way. An efficient new method was developed for simulating the robot in its visual environment. This allowed controllers to be evaluated in a simulation connected to the FPTA. The controllers then transferred seamlessly to the real world. The circuit replication issue was also addressed in experiments where circuits were evolved to be able to function correctly in multiple areas of the FPTA. A methodology was developed to analyse the evolved circuits which provided insights into their operation. Comparative experiments demonstrated the superior evolvability of the transistor array medium.

关键词： Evolvable hardware evolutionary robotics evolved analog circuits incremental evolution visually guided behaviour word recognition field programmable transistor array feature selection evolved visual features dimensionality reduction

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Behavioral specialization emerges from the embodiment of a robotic swarm

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ARTIFICIAL LIFE AND robotics 2020年第4期25卷 495-502页

作者： Hiraga, Motoaki Tamura, Yasumasa Ohkura, Kazuhiro Hiroshima Univ Grad Sch Engn Hiroshima Japan Tokyo Inst Technol Sch Comp Dept Comp Sci Tokyo Japan

This paper focuses on the effect of the embodiment of robots on collective behavior in robotic swarms. The research field of swarm robotics emphasizes the importance of the embodiment of robots;however, only a few studies have discussed how it influences the collective behavior of a robotic swarm. In this paper, a path-formation task is performed by robotic swarms in computer simulations with and without considering collisions among robots to discuss the effect of the robot embodiment. Additionally, the experiments were performed with varying the size of robots. The robot controllers were obtained by an evolutionary robotics approach. The results show that the robot collisions would affect not only the performance of the robotic swarm but also the emergent behavior to accomplish the task. The robot collisions seem to provide feedback on robotic swarms to emerge the division of labor among robots to manage congestion.

关键词： Swarm robotics evolutionary robotics Robot collisions Robot embodiment

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Crowded Environment Navigation with NEAT: Impact of Perception Resolution on Controller Optimization

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2021年第2期101卷 36-36页

作者： Seriani, Stefano Marcini, Luca Caruso, Matteo Gallina, Paolo Medvet, Eric Univ Trieste Dept Engn & Architecture Trieste Italy

Crowd navigation with autonomous systems is a topic which has seen a rapid increase in interest recently. While it appears natural to humans, being able to reach a target can prove difficult or impossible to a mobile robot because of the safety issues related to collisions with people. In this work we propose an approach to control a robot in a crowded environment;the method employs an Artificial Neural Network (ANN) that is trained with the NeuroEvolution of Augmented Topologies (NEAT) method. Models for the kinematics, perception, and cognition of the robot are presented. In particular, perception is based on a raycasting model which is tailored on the ANN. An in-depth analysis of a number of parameters of the environment and the robot is performed and a comparative analysis is presented;finally, results of the performance of the controller trained with NEAT are compared to those of a human driver who takes over the controller itself. Results show that the intelligent controller is able to perform on par with the human, within the simulated environment.

关键词： Artificial neural networks evolutionary robotics NEAT Crowd navigation Robot controller

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Learning directed locomotion in modular robots with evolvable morphologies

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APPLIED SOFT COMPUTING 2021年 111卷 107688-107688页

作者： Lan, Gongjin De Carlo, Matteo van Diggelen, Fuda Tomczak, Jakub M. Roijers, Diederik M. Eiben, A. E. Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China Vrije Univ Amsterdam Dept Comp Sci Amsterdam Netherlands Vrije Univ Brussel AI Lab Brussels Belgium HU Univ Appl Sci Utrecht Inst ICT Utrecht Netherlands

The vision behind this paper looks ahead to evolutionary robot systems where morphologies and controllers are evolved together and 'newborn' robots undergo a learning process to optimize their inherited brain for the inherited body. The specific problem we address is learning controllers for the task of directed locomotion in evolvable modular robots. To this end, we present a test suite of robots with different shapes and sizes and compare two learning algorithms, Bayesian optimization and HyperNEAT. The experiments in simulation show that both methods obtain good controllers, but Bayesian optimization is more effective and sample efficient. We validate the best learned controllers by constructing three robots from the test suite in the real world and observe their fitness and actual trajectories. The obtained results indicate a reality gap, but overall the trajectories are adequate and follow the target directions successfully. (C) 2021 Published by Elsevier B.V.

关键词： evolutionary robotics Evolvable morphologies Modular robots Bayesian optimization Directed locomotion

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Shape Changing Robots: Bioinspiration, Simulation, and Physical Realization

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ADVANCED MATERIALS 2021年第19期33卷 2002882-2002882页

作者： Shah, Dylan Yang, Bilige Kriegman, Sam Levin, Michael Bongard, Josh Kramer-Bottiglio, Rebecca Yale Univ Sch Engn & Appl Sci 9 Hillhouse Ave New Haven CT 06511 USA Univ Vermont Dept Comp Sci E428 Innovat Hall Burlington VT 05405 USA Tufts Univ Allen Discovery Ctr Dept Biol 200 Boston AveSuite 4604 Medford MA 02155 USA Harvard Univ Wyss Inst Biol Inspired Engn 3 Blackfan Cir Boston MA 02115 USA

One of the key differentiators between biological and artificial systems is the dynamic plasticity of living tissues, enabling adaptation to different environmental conditions, tasks, or damage by reconfiguring physical structure and behavioral control policies. Lack of dynamic plasticity is a significant limitation for artificial systems that must robustly operate in the natural world. Recently, researchers have begun to leverage insights from regenerating and metamorphosing organisms, designing robots capable of editing their own structure to more efficiently perform tasks under changing demands and creating new algorithms to control these changing anatomies. Here, an overview of the literature related to robots that change shape to enhance and expand their functionality is presented. Related grand challenges, including shape sensing, finding, and changing, which rely on innovations in multifunctional materials, distributed actuation and sensing, and somatic control to enable next-generation shape changing robots are also discussed.

关键词： anatomical homeostasis evolutionary robotics morphing robots reconfigurable robots regeneration smart materials soft robotics synthetic morphogenesis

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Bootstrapped Neuro-Simulation for complex robots

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robotics AND AUTONOMOUS SYSTEMS 2021年 136卷 103708-103708页

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

Robotic simulators are often used to speed up the evolutionary robotics (ER) process. Most simulation approaches are based on physics modelling. However, physics-based simulators can become complex to develop and require prior knowledge of the robotic system. robotics simulators can be constructed using Machine Learning techniques, such as Artificial Neural Networks (ANNs). ANN-based simulator development usually requires a lengthy behavioural data collection period before the simulator can be trained and used to evaluate controllers during the ER process. The Bootstrapped Neuro-Simulation (BNS) approach can be used to simultaneously collect behavioural data, train an ANN-based simulator and evolve controllers for a particular robotic problem. This paper investigates proposed improvements to the BNS approach and demonstrates the viability of the approach by optimising gait controllers for a Hexapod and Snake robot platform. (C) 2020 Published by Elsevier B.V.

关键词： evolutionary robotics Coevolution Simulator Artificial Neural Networks Snake robot Hexapod robot Bootstrapped Neuro-Simulation

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Simulating the evolution of bipedalism and the absence of static bipedal hexapods

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BIOINSPIRATION & BIOMIMETICS 2021年第5期16卷 056012-056012页

作者： Rong, Chunyan Zhu, Jiahui Giardina, Fabio Rosendo, Andre ShanghaiTech Univ Living Machines Lab 393 Mid Huaxia Rd Shanghai Peoples R China Harvard Univ John A Paulson Sch Engn & Appl Sci Boston MA 02115 USA

In nature, very few animals locomote on two legs. Static bipedalism can be found in four limbed and five limbed animals like dogs, cats, birds, monkeys and kangaroos, but it cannot be seen in hexapods or other multi-limbed animals. In this paper, we present a simulation with a novel perspective on the evolution of static bipedalism, with a virtual creature evolving its body and controllers, and we apply an evolutionary algorithm to explore the locomotion transition from octapods to bipods. We find that the presence of four limbs in the evolutionary trajectory of the creature scaffolds a parametric jump that enables bipedalism, and shows that hexapods, without undergoing such transformation, struggle to evolve into bipeds. An analysis of the transitional parameters points to the role of a shorter femur length in helping maintain the stability of the body, and the tibia length is responsible for improving the forward speed.

关键词： bipedalism evolutionary robotics multi-legged locomotion

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Novelty Search makes Evolvability Inevitable

Novelty Search makes Evolvability Inevitable

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Genetic and evolutionary Computation Conference (GECCO)

作者： Doncieux, Stephane Paolo, Giuseppe Laflaquiere, Alban Coninx, Alexandre Sorbonne Univ CNRS Inst Syst Intelligents & Robot ISIR F-75005 Paris France SoftBank Robot Europe AI Lab Paris France

ISBN: (纸本)9781450371285

Evolvability is an important feature that impacts the ability of evolutionary processes to find interesting novel solutions and to deal with changing conditions of the problem to solve. The estimation of evolvability is not straight-forward and is generally too expensive to be directly used as selective pressure in the evolutionary process. Indirectly promoting evolvability as a side effect of other easier and faster to compute selection pressures would thus be advantageous. In an unbounded behavior space, it has already been shown that evolvable individuals naturally appear and tend to be selected as they are more likely to invade empty behavior niches. Evolvability is thus a natural byproduct of the search in this context. However, practical agents and environments often impose limits on the reachable behavior space. How do these boundaries impact evolvability? In this context, can evolvability still be promoted without explicitly rewarding it? We show that Novelty Search implicitly creates a pressure for high evolvability even in bounded behavior spaces, and explore the reasons for such a behavior. More precisely we show that, throughout the search, the dynamic evaluation of novelty rewards individuals which are very mobile in the behavior space, which in turn promotes evolvability.

关键词： Novelty search evolutionary robotics evolvability behavior space

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Learning behaviour-performance maps with meta-evolution

Learning behaviour-performance maps with meta-evolution

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Genetic and evolutionary Computation Conference (GECCO)

作者： Bossens, David M. Mouret, Jean-Baptiste Tarapore, Danesh Univ Southampton Southampton Hants England Univ Lorraine INRIA CNRS Nancy France

ISBN: (纸本)9781450371285

The MAP-Elites quality-diversity algorithm has been successful in robotics because it can create a behaviorally diverse set of solutions that later can be used for adaptation, for instance to unanticipated damages. In MAP-Elites, the choice of the behaviour space is essential for adaptation, the recovery of performance in unseen environments, since it defines the diversity of the solutions. Current practice is to hand-code a set of behavioural features, however, given the large space of possible behaviour-performance maps, the designer does not know a priori which behavioural features maximise a map's adaptation potential. We introduce a new meta-evolution algorithm that discovers those behavioural features that maximise future adaptations. The proposed method applies Covariance Matrix Adaptation Evolution Strategy to evolve a population of behaviour-performance maps to maximise a meta-fitness function that rewards adaptation. The method stores solutions found by MAP-Elites in a database which allows to rapidly construct new behaviour-performance maps on-the-fly. To evaluate this system, we study the gait of the RHex robot as it adapts to a range of damages sustained on its legs. When compared to MAP-Elites with user-defined behaviour spaces, we demonstrate that the meta-evolution system learns high-performing gaits with or without damages injected to the robot.

关键词： quality-diversity algorithms behavioural diversity meta-learning evolutionary robotics damage recovery

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