Evolutionary On-Line Self-Organization of Autonomous Robots

We review recent experiments in evolutionary robotics carried out in dynamic environments and across different robotic platforms. We then introduce a new evolutionary approach where robots are evolved for their ability to adapt online. Several experiments show that this new approach is much faster, more powerful, and scalable that the traditional approach

Artificial Evolution of Adaptive Software: An Application to Autonomous Robots

Artificial evolution of computer software (evolutionary neural networks, genetic programming, evolutionary fuzzy systems, etc.) has been shown to generate software that in many cases is more performant than that designed by engineers. Evolved software performs well under the same conditions used during evolutionary training. However, in situations where unpredictable change may affect normal operation, evolved systems often fail. In this paper we describe a new approach for evolving software that remains adaptive and is therefore very robust to unpredictable change after evolution. To illustrate the idea, we present the case of evolutionary robots that quickly and reliably adapt online to several types of new situations, including sensory, environmental, and mechanical change, while still performing their task. The core of the methodology consists of evolving the mechanisms of parameter adaptation instead of the parameters themselves. We shall conclude by showing how this methodology can be applied to a variety of other situations beyond robotics

Evolution of Plastic Control Networks

Evolutionary Robotics is a powerful method to generate efficient controllers with minimal human intervention, but its applicability to real-world problems remains a challenge because the method takes long time and it requires software simulations that do not necessarily transfer smoothly to physical robots. In this paper we describe a method that overcomes these limitations by evolving robots for the ability to adapt on-line in few seconds. Experiments show that this method requires less generations and smaller populations to evolve, that evolved robots adapt in a few seconds to unpredictable change - including transfers from simulations to physical robots - and display non-trivial behaviors. Robots evolved with this method can be dispatched to other planets and to our homes where they will autonomously and quickly adapt to the specific properties of their environments if and when necessary

Incremental Evolution with Minimal Resources

This paper describes an evolutionary algorithm based on a statistical representation of populations of individuals. Experiments on robot navigation and on numerical fitness functions are presented in order to measure the performance of the algorithm compared to traditional genetic algorithms. Results show that the method is suitable for onboard online evolution because it requires low amount of memory resources. Furthermore, it allows for incremental evolution in dynamic environments in order to cope with complex tasks that require several evolutionary stages

Evolutionary Robots with Fast Adaptive Behavior in New Environments

This paper is concerned with adaptation capabilities of evolved neural controllers. A method consisting of encoding a set of local adaptation rules that synapses obey while the robot freely moves in the environment [6] is compared to a standard fixed-weight network. In the experiments presented here, the performance of the robot is measured in environments that are different in significant ways from those used during evolution. The results show that evolutionary adaptive controllers can adapt to environmental changes that involve new sensory characteristics (including transfers from simulation to reality) and new spatial relationships

Evolutionary Robots with on-line self-organization and behavioral fitness

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

Evolutionary Robotics: The Next Generation

After reviewing current approaches in Evolutionary Robotics, we point to directions of research that re likely to bring interesting results in the future. e then address two crucial aspects for future developments of Evolutionary Robotics: choice of fitness functions and scalability to real-world situations. In the first case we suggest framework to describe fitness functions, choose them according to the situation constraints, and compare available experiments in the literature on evolutionary robotics. In the second case, we suggest way to make experimental results applicable to real- world situations by evolving online continuous adaptive controllers. We also give an overview of recent experimental results showing that the suggested approaches pro- duce qualitatively superior abilities, scale up to more complex architectures, smoothly transfer from simulations to real robots and across different robotic platforms, and autonomously adapt in few seconds to several sources of strong variability that were not included during the evolutionary run

Neural Morphogenesis, Synaptic Plasticity, and Evolution

Morphology plays an important role in the computational properties of neural systems, affecting both their functionality and the way in which this functionality is developed during life. In computer-based models of neural networks, artificial evolution is often used as a method to explore the space of suitable morphologies. In this paper we critically review the most common methods used to evolve neural morphologies and argue that a more effective, and possibly biologically plausible, method consists of genetically encoding rules of synaptic plasticity along with rules of neural morphogenesis. Some preliminary experiments with autonomous robots are described in order to show the feasibility and advantages of the approach