DOSE3: Diffusion-Based Unified Out-Of-Distribution Detection on SE(3) Trajectories

Abstract

Out-Of-Distribution (OOD) detection, the task of identifying when an input falls outside the distribution seen at training time, is critical for deploying safe and reliable systems. Traditional OOD methods require retraining models whenever the in‐distribution has changed. Recent work introduces unified models for OOD detection, where metrics can be constructed from an unconditional diffusion model trained on an arbitrary dataset, and the inlier distribution can be changed without retraining the diffusion model. However, these unified approaches have been largely confined to Euclidean or latent space domains. In contrast, real‐world robotics systems often perceive and act through sequences of 6 degrees-of-freedom poses in the Special Euclidean Group SE(3), taking into account both translations and orientation changes over time. In this work, we extend OOD detection to trajectories in Special Euclidean Group in 3D SE(3) by presenting a Diffusion-based Out-of-distribution detection on SE(3) (DOSE3). DOSE3 constructs an OOD metric from the noise estimator model of a diffusion model over SE(3) to separate outlier samples from inlier distributions. We demonstrate DOSE3's strong performance on OOD detection frameworks through extensive validation on multiple real-world robotics and autonomous systems datasets, covering vehicle and robot manipulator motion trajectories.