STRiDE: State-space Riemannian Diffusion for Equivariant Planning

Evangelos Chatzipantazis, Nishanth Rao, Kostas Daniilidis

Keywords: Equivariance, Diffusion Models, Motion Planning, State Space Manifold, Riemannian Guidance, Robot Manipulation

TL;DR: State Space Motion Planning using Riemannian Diffusion Models

Abstract:

Fast and reliable motion planning is essential for robots with many degrees of freedom in com-
plex, dynamic environments. Diffusion models offer a promising alternative to classical planners
by learning informative trajectory priors. In current imitation-learning paradigms, these models are
kept lightweight—lacking encoders—and trained to overfit to a single environment. As a result,
adaptation relies solely on diffusion guidance, which fails under large execution-time changes or
varying initializations. In addition, current approaches ignore the underlying topology of the state
space thus requiring heavy guidance that dominates planning time and reduces efficiency dramati-
cally. We introduce STRiDE, a novel diffusion motion planner that operates directly on the state
space manifold and learns equivariant trajectory priors. Our approach eliminates the need for
retraining under rotations around the gravity axis and enables faster convergence using Riemannian
(rather than ambient) guidance. STRiDE delivers efficient, robust, and generalizable planning,
overcoming key limitations of existing approaches

Submission Number: 32