Adams, RyanMukherjee, Arin2024-07-182026-09-292024-07-182026-09-292024-052024-07-18http://arks.princeton.edu/ark:/88435/dsp01q524js136https://theses-dissertations.princeton.edu/handle/88435/dsp01q524js136Morphological 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.application/pdfenPrinceton University Senior Theses