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Using direct competition to select for competent controllers in evolutionary robotics

robotics AND AUTONOMOUS SYSTEMS 2006年第10期54卷 840-857页

作者： Nelson, Andrew L. Grant, Edward Androt LLC Tucson AZ 85733 USA N Carolina State Univ Ctr Robot & Intelligent Machines Raleigh NC 27695 USA

evolutionary robotics (ER) is a field of research that applies artificial evolution toward the automatic design and synthesis of intelligent robot controllers. The preceding decade saw numerous advances in evolutionary robotics hardware and software systems. However, the sophistication of resulting robot controllers has remained nearly static over this period of time. Here, we make the case that current methods of controller fitness evaluation are primary factors limiting the further development of ER. To address this, we define a form of fitness evaluation that relies on intra-population competition. In this research, complex neural networks were trained to control robots playing a competitive team game. To limit the amount of human bias or know-how injected into the evolving controllers, selection was based on whether controllers won or lost games. The robots relied on video sensing of their environment, and the neural networks required on the order of 150 inputs. This represents an order of magnitude increase in sensor complexity compared to other research in this field. Evolved controllers were tested extensively in real fully-autonomous robots and in simulation. Results and experiments are presented to characterize the training process and the acquisition of controller competency under different evolutionary conditions. (c) 2006 Elsevier B.V. All rights reserved.

关键词： evolutionary robotics learning robots evolutionary neural networks swarm robotics developmental robotics

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Onwards and Upwards, Radical Constructivism A Guest Commentary

CONSTRUCTIVIST FOUNDATIONS

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CONSTRUCTIVIST FOUNDATIONS 2010年第1期6卷 127-132页

作者： Cariani, Peter Harvard Univ Sch Med Cambridge MA 02138 USA

> Problem . How can radical constructivism gain wider recognition and acceptance? > Method . Based on informal direct observation of other social and intellectual movements, the social and psychological dynamics and organizational imperatives of radical constructivism as an intellectual movement are discussed. > Results . Various means of structuring the movement in order to gain wider acceptance are proposed. > Implications . We hope that the paper has value in helping the radical constructivism movement evaluate where it has been and where it might go in terms of wider social recognition and acceptance.

关键词： Intellectual movements history of cybernetics evolutionary robotics constructivist epistemology

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An Update Method of Computer Simulation for evolutionary robotics

An Update Method of Computer Simulation for Evolutionary Rob...

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9th International Conference on Intelligent Autonomous Systems (IAS-9)

作者： Katada, Yoshiaki Ohkura, Kazuhiro Setsunan Univ Fac Engn Neyagawa Osaka Japan Kobe Univ Fac Engn Kobe Hyogo Japan

ISBN: (纸本)9781586035952

One of advantages of evolutionary robotics over other approaches is its parallel population search. However, it generally takes an unrealistically long time to evaluate all candidate solutions by using a real robot. Thus, a technique of computer simulation is considered to be one of the most important topics in evolutionary robotics. Although it is quite difficult to provide a precise computer simulation model of a physical experiment, simulated robot/environment interaction dynamics must be synchronously consistent with the physical one during its evolution for reducing the gap between the simulated and physical one. In this paper, in order to overcome this problem, we propose an update method of simulated robot/environment interaction dynamics based on a statistical test during evolution, and then investigate the consistency between the performances of a robot in the simulated and physical environment. A series of experiments with a mobile robot illustrate the effectiveness of the proposed method in practice.

关键词： Simulation Real robot evolutionary robotics Embodied cognitive science

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Developmental analysis in evolutionary robotics

Developmental analysis in evolutionary robotics

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IEEE Mountain Workshop on Adaptive and Learning Systems

作者： Nelson, A. L. Grant, E. Androt LLC POB 44065 Tucson AZ 85733 USA North Carolina State Univ Dept ECE Raleigh NC 27695 USA

ISBN: (纸本)1424401666

This paper presents a developmental analysis of robot controllers created using evolutionary robotics (ER) methods. ER uses artificial evolution to automatically design and synthesize intelligent robot controllers. An aggregate fitness function that injects relatively little a priori task knowledge into the evolving controllers was used. We analyze the course of development of robot controllers evolving to perform a competitive goal-locating task. To sample the course of evolution, controllers were taken from progressively more advanced generations, and were then tested in a novel environment. Developments and changes in the controllers' abilities and competencies were identified and correlated with overall controller fitness. As evolution progressed, it was found that robots evolved more complex high-level behaviors that were not explicitly selected for by the fitness function.

关键词： evolutionary robotics developmental robotics evolutionary neural networks swarm robotics

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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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Accelerating Self-Modeling in Cooperative Robot Teams

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2009年第2期13卷 321-332页

作者： Bongard, Josh C. Univ Vermont Dept Comp Sci Burlington VT 05405 USA

One of the major obstacles to achieving robots capable of operating in real-world environments is enabling them to cope with a continuous stream of unanticipated situations. In previous work, it was demonstrated that a robot can autonomously generate self-models, and use those self-models to diagnose unanticipated morphological change such as damage. In this paper, it is shown that multiple physical quadrupedal robots with similar morphologies can share self-models in order to accelerate modeling. Further, it is demonstrated that quadrupedal robots which maintain separate self-modeling algorithms but swap self-models perform better than quadrupedal robots that rely on a shared self-modeling algorithm. This finding points the way toward more robust robot teams: it robot can diagnose and recover from unanticipated situations faster by drawing on the previous experiences of the other robots.

关键词： Collective robotics evolutionary robotics self-modeling

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Genetic Team Composition and Level of Selection in the Evolution of Cooperation

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2009年第3期13卷 648-660页

作者： Waibel, Markus Keller, Laurent Floreano, Dario Ecole Polytech Fed Lausanne Sch Engn Lab Intelligent Syst Lausanne Switzerland Univ Lausanne Dept Ecol & Evolut Biophore CH-1015 Lausanne Switzerland

In cooperative multiagent systems, agents interact to solve tasks. Global dynamics of multiagent teams result from local agent interactions, and are complex and difficult to predict. evolutionary computation has proven a promising approach to the design of such teams. The majority of current studies use teams composed of agents with identical control rules ("genetically homogeneous teams") and select behavior at the team level ("team-level selection"). Here we extend current approaches to include four combinations of genetic team composition and level of selection. We compare the performance of genetically homogeneous teams evolved with individual-level selection, genetically homogeneous teams evolved with team-level selection, genetically heterogeneous teams evolved with individual-level selection, and genetically heterogeneous teams evolved with team-level selection. We use a simulated foraging task to show that the optimal combination depends on the amount of cooperation required by the task. Accordingly, we distinguish between three types of cooperative tasks and suggest guidelines for the optimal choice of genetic team composition and level of selection.

关键词： Altruism artificial evolution cooperation evolutionary robotics fitness allocation multiagent systems (MAS) team composition

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Towards group transport by swarms of robots

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2009年第1-2期1卷 1-13页

作者： Gross, Roderich Dorigo, Marco Ecole Polytech Fed Lausanne Lab Syst Robot EPFL STI IMT LSRO Stn 9 CH-1015 Lausanne Switzerland Univ Libre Bruxelles IRIDIA CoDE B-1050 Brussels Belgium

We examine the ability of a swarm robotic system to transport cooperatively objects of different shapes and sizes. We simulate a group of autonomous mobile robots that can physically connect to each other and to the transported object. Controllers - artificial neural networks - are synthesised by an evolutionary algorithm. They are trained to let the robots self-assemble, that is, organise into collective physical structures and transport the object towards a target location. We quantify the performance and the behaviour of the group. We show that the group can cope fairly well with objects of different geometries as well as with sudden changes in the target location. Moreover, we show that larger groups, which are made of up to 16 robots, make possible the transport of heavier objects. Finally, we discuss the limitations of the system in terms of task complexity, scalability and fault tolerance and point out potential directions for future research.

关键词： artificial neural networks bio-inspired computation collective structures cooperation cooperative transport evolutionary robotics foraging group transport modular robots object manipulation reconfigurable robots self-assembling robots self-assembly swarm intelligence swarm robotics

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Evolving Self-Assembly in Autonomous Homogeneous Robots: Experiments with Two Physical Robots

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ARTIFICIAL LIFE 2009年第4期15卷 465-484页

作者： Ampatzis, Christos Tuci, Elio Trianni, Vito Christensen, Anders Lyhne Dorigo, Marco European Space Agcy Adv Concepts Team Estec NL-2200 AG Noordwijk Netherlands ISTC CNR I-00185 Rome Italy DCTI ISCTE P-1649026 Lisbon Portugal Univ Libre Bruxelles CoDE IRIDIA B-1050 Brussels Belgium

This research work illustrates an approach to the design of controllers for self-assembling robots in which the self-assembly is initiated and regulated by perceptual cues that are brought forth by the physical robots through their dynamic interactions. More specifically, we present a homogeneous control system that can achieve assembly between two modules (two fully autonomous robots) of a mobile self-reconfigurable system without a priori introduced behavioral or morphological heterogeneities. The controllers are dynamic neural networks evolved in simulation that directly control all the actuators of the two robots. The neurocontrollers cause the dynamic specialization of the robots by allocating roles between them based solely on their interaction. We show that the best evolved controller proves to be successful when tested on a real hardware platform, the swarm-bot. The performance achieved is similar to the one achieved by existing modular or behavior-based approaches, also due to the effect of an emergent recovery mechanism that was neither explicitly rewarded by the fitness function, nor observed during the evolutionary simulation. Our results suggest that direct access to the orientations or intentions of the other agents is not a necessary condition for robot coordination: Our robots coordinate without direct or explicit communication, contrary to what is assumed by most research work in collective robotics. This work also contributes to strengthening the evidence that evolutionary robotics is a design methodology that can tackle real-world tasks demanding fine sensory-motor coordination.

关键词： Self-assembly role allocation neural network artificial evolution evolutionary robotics

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Bio-inspired grasp control in a robotic hand with massive sensorial input

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BIOLOGICAL CYBERNETICS 2009年第2期100卷 109-128页

作者： Ascari, Luca Bertocchi, Ulisse Corradi, Paolo Laschi, Cecilia Dario, Paolo Scuola Super Sant Anna Ctr Excellence Informat Commun Engn CEIIC Pisa Italy HENESIS SRL Parma Italy Scuola Super Sant Anna Ctr Res Micro & Nanoengn CRIM Pisa Italy Scuola Super Sant Anna ARTS Lab Pisa Italy

The capability of grasping and lifting an object in a suitable, stable and controlled way is an outstanding feature for a robot, and thus far, one of the major problems to be solved in robotics. No robotic tools able to perform an advanced control of the grasp as, for instance, the human hand does, have been demonstrated to date. Due to its capital importance in science and in many applications, namely from biomedics to manufacturing, the issue has been matter of deep scientific investigations in both the field of neurophysiology and robotics. While the former is contributing with a profound understanding of the dynamics of real-time control of the slippage and grasp force in the human hand, the latter tries more and more to reproduce, or take inspiration by, the nature's approach, by means of hardware and software technology. On this regard, one of the major constraints robotics has to overcome is the real-time processing of a large amounts of data generated by the tactile sensors while grasping, which poses serious problems to the available computational power. In this paper a bio-inspired approach to tactile data processing has been followed in order to design and test a hardware-software robotic architecture that works on the parallel processing of a large amount of tactile sensing signals. The working principle of the architecture bases on the cellular nonlinear/neural network (CNN) paradigm, while using both hand shape and spatial-temporal features obtained from an array of microfabricated force sensors, in order to control the sensory-motor coordination of the robotic system. Prototypical grasping tasks were selected to measure the system performances applied to a computer-interfaced robotic hand. Successful grasps of several objects, completely unknown to the robot, e.g. soft and deformable objects like plastic bottles, soft balls, and Japanese tofu, have been demonstrated.

关键词： ROBOT hands ROBOTS -- Control systems evolutionary robotics robotics

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