In this paper we propose and justify a methodology for the development of the control systems, or `cognitive architectures', of autonomous mobile robots. We argue that the design by hand of such control systems becomes prohibitively difficult as complexity increases. We discuss an alternative approach, involving artificial evolution, where the basic building blocks for cognitive architectures are adaptive noise-tolerant dynamical neural networks, rather than programs. These networks may be recurrent, and should operate in real time. Evolution should be incremental, using an extended and modified version of genetic algorithms. We nally propose that, sooner rather than later, visual processing will be required in order for robots to engage in non-trivial navigation behaviours. Time constraints suggest that initial architecture evaluations should be largely done in simulation. The pitfalls of simulations compared with reality are discussed, together with the importance of incorporating noise. To support our claims and proposals, we present results from some preliminary experiments where robots which roam office-like environments are evolved.

The pitfalls of naive robot simulations have been recognised for areas such as evolutionary robotics. It has been suggested that carefully validated ispell slides.tex simulations with a proper treatment of noise may overcome these problems. This paper reports the results of experiments intended to test some of these claims. A simulation was constructed of a two-wheeled Khepera robot with IR and ambient light sensors. This included detailed mathematical models of the robot-environment interaction dynamics with empirically determined parameters. Artificial evolution was used to develop recurrent dynamical network controllers for the simulated robot, for obstacle-avoidance and light-seeking tasks, using different levels of noise in the simulation. The evolved controllers were down-loaded onto the real robot and the correspondence between behaviour in simulation and in reality was tested. The level of correspondence varied according to how much noise was used in the simulation, with very g...

This paper will look at an evolutionary approach to robotics; partly at pragmatic issues, but primarily at theoretical issues associated with the evolutionary algorithms which are appropriate. Genetic Algorithms are not suitable in their usual form for the evolution of cognitive structures, which must be in an incremental fashion. SAGA -- Species Adaptation Genetic Algorithms -- is a conceptual framework for extending GAs to variable length genotypes, where evolution allows a species of individuals to evolve from simple to more complex. In the context of species evolution the metaphor of hill-crawling as opposed to hill-climbing is introduced,

... methodology for the automatic design of robotic controllers. EE is an evolutionary robotics (ER) technique that avoids the pitfalls of the simulate-and-transfer method, allows the speed-up of evaluation time by utilizing parallelism, and is particularly suited to future work on multi-agent behaviors. In EE, an evolutionary algorithm is distributed amongst and embodied within a population of physical robots that reproduce with one another while situated in the task environment. We have built a population of eight robots and successfully implemented our first experiments. The controllers evolved by EE compare favorably to hand-designed solutions for a simple task. We detail our methodology, report our initial results, and discuss the application of EE to more advanced and distributed robotics tasks.

This paper explains in detail a biologically inspired encoding scheme for the artificial evolution of neural network robot controllers. Under the scheme, an individual cell divides and moves, in response to protein interactions with an artificial genome, to form a multi-cellular `organism'. After differentiation dendrites grow out of each cell, guided by chemically sensitive growth cones, to form connections between the cells. The resultant network is then interpreted as a recurrent neural network robot controller. Results are given of preliminary experiments to evolve robot controllers for both corridor following and object avoidance tasks. Keywords: Artificial Evolution, Morphogenesis, Evolutionary Robotics. 1 Introduction Many people have noted the difficulties involved in designing control architectures for robots by hand ([10], [1]) and as robots and the behaviours we demand of them become more complicated these difficulties can only increase. Evolutionary robotics is an attempt...

This paper presents an analysis of an artificially evolved dynamical network-based control system for a simulated autonomous mobile robot engaged in simple visually guided tasks.

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. © 2002 Elsevier Science B.V. All rights reserved.

. This paper describes experiments in which neural network control architectures were evolved in minimal simulation for an octopod robot. The robot is around 30cm long and has 4 infra red sensors that point ahead and to the side, various bumpers and whiskers, and ten ambient light sensors positioned strategically around the body. Each of the robot's eight legs is controlled by two servo motors, one for movement in the horizontal plane, and one for movement in the vertical plane, which means that the robots motors have a total of sixteen degrees of freedom. The aim of the experiments was to evolve neural network control architectures that would allow the robot to wander around its environment avoiding objects using its infra-red sensors and backing away from objects that it hits with its bumpers. This is a hard behaviour to evolve when one considers that in order to achieve any sort of coherent movement the controller has to control not just one or two motors in a coordinated fashion bu...

This paper provides a high-level review of current and recent work in the use of genetic algorithm based techniques to develop sensorimotor control systems for autonomous agents. It focuses on network-based controllers and genetic encoding issues associated with them. The paper closes with a discussion of the possibility of using arti cial evolutionary techniques to help tackle more specifically scientific questions about natural sensorimotor systems.

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