In this paper, we introduce a self-assembling and self-organizing artifact, called a swarm-bot, composed of a swarm of s-bots, mobile robots with the ability to connect to and to disconnect from each other. We discuss the challenges involved in controlling a swarm-bot and address the problem of synthesizing controllers for the swarm-bot using artificial evolution.

We address two issues in Evolutionary Robotics, namely the genetic encoding and the performance criterion, also known as fitness function. For the first aspect, we suggest to encode mechanisms for parameter self-organization, instead of the parameters themselves as in conventional approaches. We argue that the suggested encoding generates systems that can solve more complex tasks and are more robust to unpredictable sources of change. We support our arguments with a set of experiments on evolutionary neural controller for physical robots and compare them to conventional encoding. In addition, we show that when also the genetic encoding is left free to evolve, artificial evolution will select to exploit mechanisms of self-organization. For the second aspect, we shall discuss the role of the performance criterion, also known as fitness function, and suggest Fitness Space as a framework to conceive fitness functions in Evolutionary Robotics. Fitness Space can be used as a guide to design fitness functions as well as to compare different experiments in Evolutionary Robotics. 1 1

We present a new type of robot concept called swarm-bot, based on cooperative and swarm intelligence, that was developed within an interdisciplinary project sponsored by the Future and Emerging Technologies of the European Commission. A swarm-bot is an assembly of several mobile robots (called s-bots), which can operate both autonomously and as a group. The unique feature of the project is that s-bots can exploit physical interconnections to selfassemble into a bigger entity, a swarm-bot, capable of tackling environmental challenges that are too difficult for a single s-bot. The paper describes the development of the concept and gives an overview of the mechanical and electronic features of the first prototype. It also presents a physics-based simulator suitable to investigate time-consuming adaptive algorithms and shows examples of cooperative behaviors both in simulation and in hardware.

this paper we report on our recent work evolving controllers for robots which are required to work as a team. The word team ' has been used in a variety of senses in both the multi-robot and the ethology literature, so it is appropriate to start the paper with a denition. We will adopt the denition given by Anderson & Franks (2001) in their recent review of team behaviour in animal societies. They identify three dening features of team behaviour. First, individuals make dierent contributions to task success, i.e. they must perform dierent sub-tasks or roles (this does not preclude more than one individual adopting the same role; there may be more individuals than roles). Second, individual roles or sub-tasks are interdependent (or interlocking'), requiring structured cooperation; individuals operate concurrently, coordinating their dierent contributions in order to complete the task. Finally, a team's organizational structure persists over time, although its individuals may be substituted or swap roles (Anderson & Franks 2001)

Abstract. We describe a set of preliminary experiments to evolve spiking neural controllers for a vision-based mobile robot. All the evolutionary experiments are carried out on physical robots without human intervention. After discussing how to implement and interface these neurons with a physical robot, we show that evolution finds relatively quickly functional spiking controllers capable of navigating in irregularly textured environments without hitting obstacles using a very simple genetic encoding and fitness function. Neuroethological analysis of the network activity let us understand the functioning of evolved controllers and tell the relative importance of single neurons independently of their observed firing rate. Finally, a number of systematic lesion experiments indicate that evolved spiking controllers are very robust to synaptic strength decay that typically occurs in hardware implementations of spiking circuits. 1 Spiking Neural Circuits The great majority of biological neurons communicate by sending pulses along

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.

We show that complex visual tasks, such as position and size invariant shape recognition and navigation in the environment, can be tackled with simple architectures generated by a co-evolutionary process of active vision and feature selection. Behavioral machines equipped with primitive vision systems and direct pathways between visual and motor neurons are evolved while freely interacting with their environments. We describe the application of this methodology in three sets of experiments, namely shape discrimination, car driving, and robot navigation. We show that these systems develop sensitivity to a number of oriented, retinotopic, visual features oriented edges, corners, height – and a behavioral repertoire to locate, bring, and keep these features in sensitive regions of the vision system, resembling strategies observed in simple insects.

We describe an evolutionary approach to active vision systems for dynamic feature selection. After summarizing recent work on evolution of a simulated active retina for complex shape discrimination, we describe in detail experiments that extend this approach to an all-terrain mobile robot equipped with a mobile camera. We show that evolved robots are capable of selecting simple visual features and actively maintaining them on the same retinal position, which largely simplifies the “recognition ” task, in order to generate efficient navigation trajectories with an extremely simple neural control system. Analysis of evolved solutions indicates that robots develop a simple

This paper presents an approach to solve the symbol grounding problem within the framework of embodied cognitive science. It will be argued that symbolic structures can be used within the paradigm of embodied cognitive science by adopting an alternative definition of a symbol. In this alternative definition, the symbol may be viewed as a structural coupling between an agent's sensorimotor activations and its environment. A robotic experiment is presented in which mobile robots develop a symbolic structure from scratch by engaging in a series of language games. In this experiment it is shown that robots can develop a symbolic structure with which they can communicate the names of a few objects with a remarkable degree of success. It is further shown that, although the referents may be interpreted differently on different occasions, the objects are usually named with only one form.

We are interested in the construction of ecological models of the evolution of learning behaviour using methodological tools developed in the eld of evolutionary robotics. In this paper, we explore the applicability of integrated (i.e., non-modular) neural networks with xed connection weights and simple \leaky-integrator" neurons as controllers for autonomous learning robots. In contrast to Yamauchi and Beer (1994a), we show that such a control system is capable of integrating reactive and learned behaviour without needing explicitly hand-designed modules, dedicated to particular behaviour, or an externally introduced reinforcement signal. In our model, evolutionary and ecological contingencies structure the controller and the behavioural responses of the robot. This allows us to concentrate on examining the conditions under which learning behaviour evolve.