Factored Diffusion Policies:Compositionally Generalized Robot Control with a Single Score Network

ArXi:2605.22596v1 Announce Type: new Robotic tasks are typically specified by a tuple of factors, such as the object to be grasped, the obstacles to be avoided, the color of the target, and so on. Collecting expert nstrations for every combination of factor values grows combinatorially. We present factored diffusion policies: a single shared diffusion network trained with per-factor null-token dropout, whose score decomposes additively across factors at inference.