The ambulatory capabilities of legged robots offer the potential for access to dangerous and uneven terrain without a risk to human life. However, while machine learning has proven effective at training such robots to walk, a significant limitation of such approaches is that controllers trained for a specific robot are likely to fail when transferred to a robot with a slightly different morphology. This paper confronts this challenge with a novel strategy: Instead of training a controller for a particular quadruped morphology, it evolves a special function (through a method called HyperNEAT) that takes morphology as input and outputs an entire neural network controller fitted to the specific morphology. Once such a relationship is learned the output controllers are able to work on a diversity of different morphologies. Highlighting the unique potential of such an approach, in this paper a neural controller evolved for three different robot morphologies, which differ in the length of their legs, can interpolate to never-seen intermediate morphologies without any further training. Thus this work suggests a new research path towards learning controllers for whole ranges of morphologies: Instead of learning controllers themselves, it is possible to learn the relationship between morphology and control.
['Sebastian Risi', 'Kenneth O. Stanley']
Confronting the challenge of learning a flexible neural controller for a diversity of morphologies