Controllable Motion Generation Via Diffusion Modal Coupling

Abstract

Diffusion models are increasingly used in robotics to represent multi-modal distributions over system states and behaviors, but precise control of generated outcomes without degrading physical realism remains challenging. This paper introduces a controllable diffusion framework that (i) replaces the standard unimodal Gaussian prior with an explicit multi-modal prior, and (ii) enforces modal coupling between prior components and principal data modes through novel forward and reverse diffusion processes. Sampling is initialized directly from a selected prior mode aligned with task constraints, avoiding train–test mismatch and manifold drift commonly induced by post-hoc guidance. Empirical evaluations on motion prediction (Waymo Dataset) and multi-task control (Maze2D) show consistent improvements over guidance-based baselines in fidelity, diversity, and controllability. These results indicate that multi-modal priors with strong modal coupling provide a scalable basis for controllable motion generation in robotics.