The Evolution of Complexity in Autonomous Robots

Evolutionary robotics–the use of evolutionary algorithms to automate the production of autonomous robots–has been an active area of research for two decades. However, previous work in this domain has been limited by the simplicity of the evolved robots and the task environments within which they are able to succeed. This dissertation aims to address these challenges by developing techniques for evolving more complex robots. Particular focus is given to methods which evolve not only the control policies of manually-designed robots, but instead evolve both the control policy and physical form of the robot. These techniques are presented along with their application to investigating previously unexplored relationships between the complexity of evolving robots and the task environments within which they evolve.

Dynamic Resolution in the Co-Evolution of Morphology and Control

Joshua Evan Auerbach

Evolutionary robotics is a promising approach to overcoming the limitations and biases of human designers in producing control strategies for autonomous robots. However, most work in evolutionary robotics remains solely concerned with optimizing control strategies for existing morphologies. By contrast, natural evolution, the only process that has produced intelligent agents to date, may modify both the control (brain) and morphology (body) of organisms. Therefore, co-evolving morphology along with control may provide a better path towards realizing intelligent robots. This paper presents a novel method for co-evolving morphology and control using CPPN-NEAT. This method is capable of dynamically adjusting the resolution at which components of the robot are created: a large number of small sized components may be present in some body locations while a smaller number of larger sized components is present in other locations. Advantages of this capability are demonstrated on a simple task, and implications for using this methodology to create more complex robots are discussed.

MIT Press2010

The evolution of communication in robot societies

Sara Mitri

Communication is fundamental to life on earth. All social organisms, from bacteria to humans, use communicative signals to coordinate their behaviors with members of their own and other species. Despite its key role in social organization, many questions regarding the evolution of communication are yet to be answered. This is in part due to the difficulty of conducting experimental evolution on social species, and the challenges in experimentally manipulating and measuring signaling and response strategies in communicating organisms. In this thesis, we circumvent these problems by using a system of experimental evolution with groups of foraging robots that could emit and perceive light to communicate. With this system, we have explored how communication can emerge and how different evolutionary conditions can determine the level of reliability of evolving signals. Our experiments revealed that foraging robots initially produced inadvertent cues providing information to other robots about the location of the food. This resulted in increased foraging efficiency, and consequently, in competition near the food, which drove the co-evolution of signaling and response strategies. The reliability of the resulting communication system was found to depend on the level of relatedness between robots in a group and the level at which they were selected. Robots that were highly related or selected at the group level evolved reliable signals. In contrast, when relatedness between robots in a group was low and selection was acting at the level of the individual, robots were selected to suppress the inadvertent cues produced while foraging. However, because of the effect of mutations, these cues were never completely suppressed and some variability in signaling was maintained. Because similar co-evolutionary processes should be common in natural systems, our findings explain why communicative strategies are so variable in many animal species when interests between them conflict. They also predict that relatedness will play an important role in the evolution of signal reliability in natural systems of communication. Additionally, our analyses have led us to devise a quantitative measure of signal reliability, which may be applied to measure reliability in natural systems of communication. The results of this study, together with an extensive review of the literature, illustrate how evolutionary robotic systems can be used to explore issues that cannot easily be studied experimentally with living organisms, and thus contribute to our understanding of biological systems.

EPFL2009

The evolution of communication in robot societies

Sara Mitri

EPFL2009