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Environment-driven distributed evolutionary adaptation in a population of autonomous robotic agents

MATHEMATICAL AND COMPUTER MODELLING OF DYNAMICAL SYSTEMS 2012年第1期18卷 101-129页

作者： Bredeche, Nicolas Montanier, Jean-Marc Liu, Wenguo Winfield, Alan F. T. TAO Univ Paris Sud INRIA CNRS F-91405 Orsay France Univ W England Bristol Robot Lab Bristol BS16 1QY Avon England

This article is concerned with a fixed-size population of autonomous agents facing unknown, possibly changing, environments. The motivation is to design an embodied evolutionary algorithm that can cope with the implicit fitness function hidden in the environment so as to provide adaptation in the long run at the level of population. The proposed algorithm, termed MEDEA, is shown to be both efficient in unknown environments and robust to abrupt and unpredicted changes in the environment. The emergence of consensus towards specific behavioural strategies is examined, with a particular focus on algorithmic stability. Finally, a real-world implementation of the algorithm is described with a population of 20 real-world e-puck robots.

关键词： evolutionary robotics artificial life open-ended evolution swarm intelligence

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Robots that specialize and make exchanges

Robots that specialize and make exchanges

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IEEE Symposium on Artificial Life

作者： Domenico Parisi Giovanni Sirio Carmantini Institute of Cognitive Sciences and Technologies National Research Council Rome School of Mathematics and Computing University of Plymouth

ISBN: (纸本)9781467358620

Specialization and exchange are two important specifically human adaptations that are at the origin of much of the complexity of human social life and of human societies. The paper describes simple simulated robots that evolve in environments containing either two types of food or both food and tools and tries to establish in which environments specialization emerges, what is the relation between exchange and specialization and what their advantages are.

关键词： Specialization Exchange evolutionary robotics

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Body Building: Hatching Robot Organisms

Body Building: Hatching Robot Organisms

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IEEE International Conference on Evolvable Systems

作者： Berend Weel Evert Haasdijk A.E. Eiben VU University Amsterdam The Netherlands

ISBN: (纸本)9781467358682

We propose a new scenario for the evolution of robot morphologies based on an egg metaphor. A swarm of robots is released in a large arena in which they form organisms through a process of morphogenesis. These organisms can reproduce by fertilising eggs in their vicinity, these fertilised eggs in turn build new organisms by recruiting free modules as a 'seed'. We investigate the influence of three parameters of this evolutionary system: the time eggs wait to be fertilised, the maximum time a seed can use to recruit modules to form an organism and how long an organism lives. Specifically we investigate the influence of these parameters on the size and stability of the population of eggs, seeds and organisms. It is shown that the influence of the lifetime of an organism is the largest, and leads to many organisms, it should be set much higher than the other two. Furthermore setting the time an egg can be fertilised and the maximum time a seed is allowed to build an organism to the same value results in the most stable system regarding number of eggs and seeds. Finally setting the maximum seed time higher leads to to a slightly smaller population of bigger organisms.

关键词： Embodied Evolution Robotic Organisms evolutionary robotics robotics Robot Morphology Organism robotics Eggs Robot Seeds body building Zygote

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Exploratory analysis of an on-line evolutionary algorithm in simulated robots

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evolutionary INTELLIGENCE 2012年第4期5卷 213-230页

作者： Haasdijk, Evert Smit, S. K. Eiben, A. E. Vrije Univ Amsterdam Amsterdam Netherlands TNO The Hague Netherlands

In traditional evolutionary robotics, robot controllers are evolved in a separate design phase preceding actual deployment;we call this off-line evolution. Alternatively, robot controllers can evolve while the robots perform their proper tasks, during the actual operational phase;we call this on-line evolution. In this paper we describe three principal categories of on-line evolution for developing robot controllers (encapsulated, distributed, and hybrid), present an evolutionary algorithm belonging to the first category (the (mu + 1) ON-LINE algorithm), and perform an extensive study of its behaviour. In particular, we use the Bonesa parameter tuning method to explore its parameter space. This delivers near-optimal settings for our algorithm in a number of tasks and, even more importantly, it offers profound insights into the impact of our algorithm's parameters and features. Our experimental analysis of (mu + 1) ON-LINE shows that it seems preferable to try many alternative solutions and spend little effort on refining possibly faulty assessments;that there is no single combination of parameters that performs well on all problem instances and that the most influential parameter of this algorithm-and therefore the prime candidate for a control scheme-is the evaluation length tau.

关键词： evolutionary robotics On-lineevolution Scientific testing Parameter tuning Bonesa

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A Hormone-Based Controller for Evaluation-Minimal Evolution in Decentrally Controlled Systems

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ARTIFICIAL LIFE 2012年第2期18卷 165-198页

作者： Hamann, Heiko Schmickl, Thomas Crailsheim, Karl Karl Franzens Univ Graz Dept Zool Artificial Life Lab A-8010 Graz Austria

One of the main challenges in automatic controller synthesis is to develop methods that can successfully be applied Lot complex tasks. The difficulty is increased even more in the case of settings with multiple interacting agents. We apply the artificial homeostatic hormone system (AHHS) approach, which is inspired by the signaling network of unicellular organisms, to control a system of several independently acting agents decentrally. The approach is designed for evaluation-minimal, artificial evolution in order to be applicable to complex modular robotics scenarios. The performance of AHHS controllers is compared with ncuroevolution of augmenting topologies (NEAT) in the coupled inverted pendulums benchmark. AHHS controllers are found to be better for multimodular settings. We analyze the evolved controllers with regard to the usage of sensory inputs and the emerging oscillations, and we give a nonlinear dynamics interpretation. The generalization of evolved controllers to initial conditions far from the original conditions is investigated and found to be good. Similarly, the performance of controllers scales well even with module numbers different from the original domain the controller was evolved for. Two reference implementations of a similar controller approach are reported and shown to have shortcomings. We discuss the related work and conclude by summarizing the main contributions of our work.

关键词： Controller synthesis hormone-endocrine system decentral control evolutionary robotics inspiration by signaling networks of unicellular organisms

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Biped locomotion control with evolved adaptive center-crossing continuous time recurrent neural networks

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NEUROCOMPUTING 2012年 86卷 86-96页

作者： Santos, Jose Campo, Angel Univ A Coruna Dept Comp Sci Coruna 15071 A Spain

We used center-crossing continuous time recurrent neural networks as central pattern generator controllers in biped robots, together with an adaptive methodology to improve the ability of the recurrent neural networks to produce rhythmic activation behaviors. The parameters of the recurrent networks are adapted or modified in run-time to reach the center-crossing condition, so the nodes get close to the most sensitive region to their input. This facilitates the evolution of the networks that act as central pattern generators to control biped structures. The robustness of the adaptive networks to produce rhythmic activation patterns was checked as well as the improvements and possibilities this adaptation may add. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Continuous time recurrent neural networks Genetic algorithms evolutionary robotics

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Linear reactive control of three-dimensional bipedal walking in the presence of noise and uncertainty

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ADAPTIVE BEHAVIOR 2012年第6期20卷 409-426页

作者： Locascio, Mark A. Solomon, Joseph H. Hartmann, Mitra J. Z. Northwestern Univ Dept Biomed Engn Evanston IL 60208 USA Northwestern Univ Dept Mech Engn Evanston IL 60208 USA

Walking control of biped robots is a challenging problem, and improving robustness to noise and uncertainty remains difficult. We recently developed a novel control framework for 3D bipedal walking that we call "linear reactive control." It is linear because control torques are computed as simple weighted sums of sensor states. It is reactive because it depends only on the model's current state. The present simulation study shows that this controller performs reliably in the presence of realistic models of joint actuation, sensor noise, and uncertainty in model and contact parameters. The controller is able to maintain a stable gait in the presence of noisy sensor inputs and low-impedance actuation. It also performs reliably on models with high uncertainty (up to 20%) in measurements of their dynamic parameters and widely varying ground contact parameters. The robustness of this controller to realistic conditions validates this method as a promising avenue for bipedal control.

关键词： evolutionary robotics bipedal walking biped robot-environment interactions reactive control

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Neural control of a modular multi-legged walking machine: Simulation and hardware

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robotics AND AUTONOMOUS SYSTEMS 2012年第2期60卷 227-241页

作者： von Twickel, Arndt Hild, Manfred Siedel, Torsten Patel, Vishal Pasemann, Frank Univ Osnabruck Dept Neurocybernet Inst Cognit Sci D-49069 Osnabruck Germany Humboldt Univ LFG Kunstliche Intelligenz Inst Informat D-10099 Berlin Germany

The modular walking machine Octavio is a bio-inspired robot designed to serve as a testbed for modular neural locomotion control. It consists of up to eight control- and energy-autonomous leg modules, each equipped with 3 active and 2 passive compliant joints and various proprioceptive sensors. Legs may be either used in single leg (treadmill) experiments or can be quickly attached to and detached from bodies with different morphologies. Body morphologies include 4-, 6- and 8-legged machines. Neurocybernetic control is developed and optimized using evolutionary techniques together with a physical simulation of the machine and its environment. This article gives an overview of the machines mechanics, electronics, firmware, configuration and control software. Simple examples demonstrate how the behavior of the simulated and the physical machines are controlled by e.g. neurobiologically motivated modular neural networks. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Modular robotics Biologically-inspired robotics evolutionary robotics Neurocybernetics Locomotion control Sensori-motor loop

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A Study on Genetic Algorithm and Neural Network for Mini-Games

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2012年第1期28卷 145-159页

作者： Wong, Sai-Keung Fang, Shih-Wei Natl Chiao Tung Univ Dept Comp Sci Hsinchu 300 Taiwan

Physics simulation and character control are two important issues in computer games. In this paper, we propose two games which are tailored for investigating some aspects of these two issues. We study on the applications of neural network and the genetic algorithm techniques for building the controllers and the controllers should be able to finish the specific tasks in the two games. The goal of the first game is that the controller can shoot a ball so that the ball collides with the other two balls one after another. The challenge of this game is that the ball should be shot from the proper position and the goal is achieved every time. The second game is a duel game and two virtual characters are controlled to fight with each other. We develop a method for verifying whether or not the skill power of the two virtual characters is balanced. The controllers of both games are evolved based on neural network and genetic algorithm in an unsupervised learning manner. We perform a comprehensive study on the performance and weaknesses of the controllers.

关键词： artificial intelligence evolutionary robotics games physics simulation skill balancing

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Synchronisation effects on the behavioural performance and information dynamics of a simulated minimally cognitive robotic agent

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BIOLOGICAL CYBERNETICS 2012年第6-7期106卷 407-427页

作者： Moioli, Renan C. Vargas, Patricia A. Husbands, Phil Univ Sussex CCNR Dept Informat Brighton BN1 9QH E Sussex England Heriot Watt Univ Sch Math & Comp Sci Edinburgh EH14 4AS Midlothian Scotland

Oscillatory activity is ubiquitous in nervous systems, with solid evidence that synchronisation mechanisms underpin cognitive processes. Nevertheless, its informational content and relationship with behaviour are still to be fully understood. In addition, cognitive systems cannot be properly appreciated without taking into account brain-body- environment interactions. In this paper, we developed a model based on the Kuramoto Model of coupled phase oscillators to explore the role of neural synchronisation in the performance of a simulated robotic agent in two different minimally cognitive tasks. We show that there is a statistically significant difference in performance and evolvability depending on the synchronisation regime of the network. In both tasks, a combination of information flow and dynamical analyses show that networks with a definite, but not too strong, propensity for synchronisation are more able to reconfigure, to organise themselves functionally and to adapt to different behavioural conditions. The results highlight the asymmetry of information flow and its behavioural correspondence. Importantly, it also shows that neural synchronisation dynamics, when suitably flexible and reconfigurable, can generate minimally cognitive embodied behaviour.

关键词： Synchronisation evolutionary robotics Oscillatory networks Transfer entropy Kuramoto model

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