Learning Neuromechanical Functions: Adaptability and Advantage in Gradient-Based Design of Morphological Computation

Abstract

Morphological computation is the idea that intelligent systems offload computation from their control systems to their morphologies. Towards the goal of designing complex passively-adaptable intelligent systems, we learn simple adaptive functions and quantify the extent to which a learned morphology induces a passive-automatic control system. Key to our approach is the neuromechanical autoencoder framework, used to co-learn the morphology and controls of a system with a gradient based approach.