Math & Kinematics

Tensor Decomposition

Tensor decomposition is the factorization of a multi-dimensional array into structured combinations of simpler components, with canonical forms including CP decomposition, Tucker decomposition, and tensor-train formats. In robotics and machine learning it is used to compress neural network weights, expose low-rank structure in multi-modal sensor data, and represent value functions or dynamics models over structured state spaces compactly.

Why it matters for physical AI

Compressing large policy and perception networks with low-rank factorizations helps fit foundation-model-scale computation onto power-constrained onboard hardware, an increasingly important lever as robot policies grow toward billions of parameters.