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We propose a modular architecture for autonomous robots which allows for the implementation of basic behavioral modules by both programming and training, and accommodates for an evolutionary development of the interconnections among modules. This architecture can implement highly complex controllers and allows for incremental shaping of the robot behavior. Our proposal is exemplified and evaluated experimentally through a number of mobile robotic tasks involving exploration, battery recharging and object manipulation.

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.

In this article we describe a methodology for evolving neurocontrollers of autonomous mobile robots without human intervention. The presentation, which spans from technological and methodological issues to several experimental results on evolution of physical mobile robots, covers both previous and recent work in the attempt to provide a unified picture within which the reader can compare the effects of systematic variations on the experimental settings. After describing some key principles for building mobile robots and tools suitable for experiments in adaptive robotics, we give an overview of different approaches to evolutionary robotics and present our methodology. We start reviewing two basic experiments showing that different environments can shape very different behaviours and neural mechanisms under very similar selection criteria. We then address the issue of incremental evolution in two different experiments from the perspective of changing environments and robot morphologies. Finally, we investigate the possibility of evolving plastic neurocontrollers and analyse an evolved neurocontroller that relies on fast and continuously changing synapses characterized by dynamic stability. We conclude by reviewing the implications of this methodology for engineering, biology, cognitive science and artificial life, and point at future directions of research.

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.