Evolving robot bodies with a sense of direction

Abstract

The joint evolution of bodies and brains (morphologies and controllers) is one of the grand challenges of Evolutionary Robotics. Related work is conducted in various morphological spaces, including, modular robots and voxel-based artificial organisms, most commonly evolving robots for a good gait, i.e., for walking as far as possible without a target, using an open-loop controller. Here we investigate a practically more relevant task: directed locomotion, i.e., the ability to walk in a target direction. To this end, we use closed-loop controllers based on an extension of the popular CPG-networks and compare them to the traditional open-loop setup, where robots are walking 'blindly', but walking in the right direction is rewarded by higher fitness. Our results disclose that the new system does not only lead to better task performance (which was expected), but also to very different morphologies and gaits. In other words, we obtain surprising results showing that adding the ability to utilise sensory feedback does not only lead to better performance, but also to different 'life forms'.