Online Gait Learning for Modular Robots with Arbitrary Shapes and Sizes

Abstract

Evolutionary robotics using real hardware is currently restricted to evolving robot controllers, but the technology for evolvable morphologies is advancing quickly. Rapid prototyping (3D printing) and automated assembly are the main enablers of robotic systems where robot offspring can be produced based on a blueprint that specifies the morphologies and the controllers of the parents. This article addresses the problem of gait learning in newborn robots whose morphology is unknown in advance. We investigate a reinforcement learning method and conduct simulation experiments using robot morphologies with different size and complexity. We establish that reinforcement learning does the job well and that it outperforms two alternative algorithms. The experiments also give insights into the online dynamics of gait learning and into the influence of the size, shape, and morphological complexity of the modular robots. These insights can potentially be used to predict the viability of modular robotic organisms before they are constructed.