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Topos: Spiking neural networks for temporal pattern recognition in complex real sounds

NEUROCOMPUTING 2008年第4-6期71卷 721-732页

作者： Gonzalez-Nalda, Pablo Cases, Blanca Univ Basque Country UPV EHU Madrid Spain

This article depicts the approach used to build the Topos application, a simulation of two-wheel robots able to discern real complex sounds. Topos is framed in the nouvelle concept of subsymbolic artificial intelligence, applied to the field of evolutionary robotics. This paper focuses on the simulation of biologically inspired artificial cochleas and spiking neural networks, in order to model the embodied control system of the robots. The method chosen to find the most appropriate parameters that determine robots' behaviour is evolutionary computation techniques, with the aim of avoiding any human intervention in this task. As an example of a real application of this technique, experiments were performed to study the ability of the robots to distinguish sounds composed of parts of real canary songs and to navigate to the recognised signal. Results obtained confirm the validity of the approach. (c) 2007 Elsevier B.V. All rights reserved.

关键词： evolutionary robotics spiking neural networks sound perception sensorimotor transformations auditory navigation

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Self-reconfigurable robots - Shape-changing cellular robots can exceed conventional robot flexibility

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IEEE robotics & AUTOMATION MAGAZINE 2007年第1期14卷 71-78页

作者： Murata, Satoshi Kurokawa, Haruhisa Tokyo Inst Technol Dept Computat Intelligence & Syst Sci Midori Ku Yokohama Kanagawa 2268502 Japan Minist Int Trade & Ind Inst Adv Ind Sci & Technol Mech Engn Lab Tokyo Japan

In this article, the concept of a cellular robot that is capable of reconfiguring itself is reviewed. This "self-reconfigurable (SR) robot" exemplifies a new trend in robotics, indeed, we can now build various kinds of SR robots with off-the-shelf technologies of processors, actuators, and sensors. These SR robots, based on modern mechatronics, are still not as adaptable as the liquid metal robot in The Terminator 2 but are just as flexible as any conventional robots

关键词： self-reconfigurable robots modular robotics shape shifting self-assembly self-repair evolutionary robotics

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Morpho Evolution With Learning Using a Controller Archive as an Inheritance Mechanism

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 2023年第2期15卷 507-517页

作者： Le Goff, Leni K. Buchanan, Edgar Hart, Emma Eiben, Agoston E. Li, Wei De Carlo, Matteo Winfield, Alan F. Hale, Matthew F. Woolley, Robert Angus, Mike Timmis, Jon Tyrrell, Andy M. Edinburgh Napier Univ Sch Comp Edinburgh EH11 4BN Midlothian Scotland Univ York Dept Elect Engn York N Yorkshire England Vrije Univ Amsterdam Dept Comp Sci NL-1081 HV Amsterdam Netherlands Univ West England Bristol Robot Lab Bristol BS16 1QY England Univ Sunderland Sch Comp Sci Sunderland England

Most work in evolutionary robotics centers on evolving a controller for a fixed body plan. However, previous studies suggest that simultaneously evolving both controller and body plan could open up many interesting possibilities. However, the joint optimization of body plan and control via evolutionary processes can be challenging in rich morphological spaces. This is because offspring can have body plans that are very different from either of their parents, leading to a potential mismatch between the structure of an inherited neural controller and the new body. To address this, we propose a framework that combines an evolutionary algorithm to generate body plans and a learning algorithm to optimize the parameters of a neural controller. The topology of this controller is created once the body plan of each offspring has been generated. The key novelty of the approach is to add an external archive for storing learned controllers that map to explicit "types" of robots (where this is defined with respect to the features of the body plan). By initiating learning from a controller with an appropriate structure inherited from the archive, rather than from a randomly initialized one, we show that both the speed and magnitude of learning increase over time when compared to an approach that starts from scratch, using two tasks and three environments. The framework also provides new insights into the complex interactions between evolution and learning.

关键词： Robots Optimization Topology Statistics Sociology Process control Aerospace electronics Embodied intelligence evolutionary robotics

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The balance between initial training and lifelong adaptation in evolving robot controllers

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 2006年第2期36卷 423-432页

作者： Walker, Joanne H. Garrett, Simon M. Wilson, Myra S. Univ Wales Dept Comp Sci Aberystwyth SY23 3DB Dyfed Wales

A central aim of robotics research is to design robots that can perform in the real world;a real world that is often highly changeable in nature. An important challenge for researchers is therefore to produce robots that can improve their performance when the environment is stable, and adapt when the environment changes. This paper reports on experiments which show how evolutionary methods can provide lifelong adaptation for robots, and how this evolutionary process was embodied on the robot itself. A unique combination of training and lifelong adaptation are used, and this paper highlights the importance of training to this approach.

关键词： evolution strategy evolutionary robotics genetic algorithm lifelong adaptation training

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Evolving autonomous robot: From controller to morphology

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2000年第2期E83D卷 200-210页

作者： Lee, WP Natl Pingtung Univ Sci & Technol Dept Management Informat Syst Pingtung Taiwan

Building robots is generally considered difficult, because the designer not only has to predict the interactions between the robot and the environment, but also has to deal with the consequent problems. In recent years, evolutionary algorithms have been proposed to synthesize robot controllers. However, admittedly, it is not satisfactory enough just to evolve the control system, because the performance of the control system depends on other hardware parameters - the robot body plan - which might include body size, wheel radius, motor time constant, etc. Therefore, the robot body plan itself should, ideally, also adapt to the task that the evolved robot is expected to accomplish. In this paper, a hybrid GP/GA framework is presented to evolve complete robot systems, including controllers and bodies, to achieve fitness-specified tasks. In order to assess the performance of the developed system, we use it with a fixed robot body plan to evolve controllers for a variety of tasks at first, then to evolve complete robot systems. Experimental results show the promise of our system.

关键词： evolutionary robotics learning robot artificial life evolutionary computing

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Autonomous evolution of dynamic gaits with two quadruped robots

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IEEE TRANSACTIONS ON robotics 2005年第3期21卷 402-410页

作者： Hornby, GS Takamura, S Yamamoto, T Fujita, M NASA Ames Res Ctr QSS Grp Inc Moffett Field CA 94035 USA Sony Corp Entertainment Robot Co Tokyo 1050004 Japan Sony Corp Intellignet Dynam Lab Tokyo 1410001 Japan

A challenging task that must be accomplished for every legged robot is creating the walking and running behaviors needed for it to move. In this paper we describe our system for autonomously evolving dynamic gaits on two of Sony's quadruped robots. Our evolutionary algorithm runs on board the robot and uses the robot's sensors to compute the quality of a gait without assistance from the experimenter. First, we show the evolution of a pace and trot gait on the OPEN-R prototype robot. With the fastest gait, the robot moves at over 10 m/min, which is more than forty body-lengths/min. While these first gaits are somewhat sensitive to the robot and environment in which they are evolved, we then show the evolution of robust dynamic gaits, one of which is used on the ERS-110, the first consumer version of AIBO.

关键词： dynamic gaits evolutionary algorithm evolutionary robotics genetic algorithm legged locomotion legged robot quadruped robot

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Evolving non-trivial behaviors on real robots: A garbage collecting robot

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robotics AND AUTONOMOUS SYSTEMS 1997年第3-4期22卷 187-198页

作者： Nolfi, S Natl Res Council Inst Psychol I-00187 Rome Italy

Recently, a new approach involving a form of simulated evolution has been proposed to build autonomous robots. However, it is still not clear if this approach is adequate for real life problems. In this paper we show how control systems that perform a non-trivial sequence of behaviors can be obtained with this methodology by "canalizing" the evolutionary process in the right direction. In the experiment described in the paper, a mobile robot was successfully trained to keep clear an arena surrounded by walls by locating, recognizing, and grasping "garbage" objects and by taking collected objects outside the arena. The controller of the robot was evolved in simulation and then downloaded and tested on the real robot. We also show that while a given amount of supervision may canalize the evolutionary process in the right direction the addition of unnecessary constraints can delay the evolution of the desired behavior. Copyright (C) 1997 Elsevier Science B.V.

关键词： autonomous robots evolutionary robotics neural networks genetic algorithms

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Online Gait Learning for Modular Robots with Arbitrary Shapes and Sizes

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ARTIFICIAL LIFE 2017年第1期23卷 80-104页

作者： Weel, Berend D'Angelo, M. Haasdijk, Evert Eiben, A. E. Vrije Univ Amsterdam Dept Comp Sci Amsterdam Netherlands Univ Roma La Sapienza Rome Italy

evolutionary robotics using real hardware is currently restricted to evolving robot controllers, but the technology for evolvable morphologies is advancing quickly. Rapid prototyping (3D printing) and automated assembly are the main enablers of robotic systems where robot offspring can be produced based on a blueprint that specifies the morphologies and the controllers of the parents. This article addresses the problem of gait learning in newborn robots whose morphology is unknown in advance. We investigate a reinforcement learning method and conduct simulation experiments using robot morphologies with different size and complexity. We establish that reinforcement learning does the job well and that it outperforms two alternative algorithms. The experiments also give insights into the online dynamics of gait learning and into the influence of the size, shape, and morphological complexity of the modular robots. These insights can potentially be used to predict the viability of modular robotic organisms before they are constructed.

关键词： evolutionary robotics embodied evolution modular robots artificial life online gait learning reinforcement learning

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Toward an Empirical Practice in Offline Fully Automatic Design of Robot Swarms

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2022年第6期26卷 1236-1245页

作者： Ligot, Antoine Cotorruelo, Andres Garone, Emanuele Birattari, Mauro Univ Libre Bruxelles IRIDIA B-1050 Brussels Belgium Univ Libre Bruxelles Serv Automatique & Anal Syst B-1050 Brussels Belgium

Due to the lack of systematic empirical analyses and comparisons of ideas and methods, a clearly established state of the art is still missing in the optimization-based design of robot swarms. In this article, we propose an experimental protocol for the comparison of fully automatic design methods. This protocol is characterized by two notable elements: 1) a way to define benchmarks for the evaluation and comparison of design methods and 2) a sampling strategy that minimizes the variance when estimating their expected performance. To define generally applicable benchmarks, we introduce the notion of mission generator: a tool to generate missions that mimic those a design method will eventually have to solve. To minimize the variance of the performance estimation, we show that, under some common assumptions, one should adopt the sampling strategy that maximizes the number of missions considered-a formal proof is provided as the supplementary material. We illustrate the experimental protocol by comparing the performance of two offline fully automatic design methods that were presented in previous publications.

关键词： evolutionary robotics experimental methodology offline automatic design optimization-based design swarm robotics

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Real-World Evolution of Robot Morphologies: A Proof of Concept

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ARTIFICIAL LIFE 2017年第2期23卷 206-235页

作者： Jelisavcic, Milan de Carlo, Matteo Hupkes, Elte Eustratiadis, Panagiotis Orlowski, Jakub Haasdijk, Evert Auerbach, Joshua E. Eiben, A. E. Vrije Univ Amsterdam Amsterdam Netherlands Univ Amsterdam Amsterdam Netherlands Univ Warsaw Warsaw Poland Champlain Coll Burlington VT USA

evolutionary robotics using real hardware has been almost exclusively restricted to evolving robot controllers, but the technology for evolvable morphologies is advancing quickly. We discuss a proof-of-concept study to demonstrate real robots that can reproduce. Following a general system plan, we implement a robotic habitat that contains all system components in the simplest possible form. We create an initial population of two robots and run a complete life cycle, resulting in a new robot, parented by the first two. Even though the individual steps are simplified to the maximum, the whole system validates the underlying concepts and provides a generic workflow for the creation of more complex incarnations. This hands-on experience provides insights and helps us elaborate on interesting research directions for future development.

关键词： evolutionary robotics embodied evolution modular robots artificial life

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