DOSE3: Diffusion-Based Unified Out-of-Distribution Detection on $\mathbb{SE}(3)$ Trajectories

Hongzhe Cheng; Tianyou Zheng; Ziyong Ma; Tianyi Zhang; Matthew Johnson-Roberson; Weiming Zhi

RA-L 20261 citations

DOSE3: Diffusion-Based Unified Out-of-Distribution Detection on $\mathbb{SE}(3)$ Trajectories

Hongzhe Cheng, Tianyou Zheng, Ziyong Ma, Tianyi Zhang, Matthew Johnson-Roberson, Weiming Zhi

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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbb {SE}(3)$</tex-math></inline-formula>, taking into account both translations and orientation changes over time. In this work, we extend OOD detection to trajectories in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Special Euclidean Group in 3D (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbb {SE}(3)$</tex-math></inline-formula>) by presenting a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/><bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iffusion-based <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ut-of-distribution detection on <inline-formula><tex-math notation="LaTeX">$\mathbb {SE}(3)$</tex-math></inline-formula> (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf {DOSE3}$</tex-math></inline-formula>). <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf {DOSE3}$</tex-math></inline-formula> constructs an OOD metric from the noise estimator model of a diffusion model over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbb {SE}(3)$</tex-math></inline-formula> to separate outlier samples from inlier distributions. We demonstrate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf {DOSE3}$</tex-math></inline-formula>'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.

BibTeX

@inproceedings{ral2026_dose3diffusionba,
  title = {DOSE3: Diffusion-Based Unified Out-of-Distribution Detection on $\mathbb{SE}(3)$ Trajectories},
  author = {Hongzhe Cheng and Tianyou Zheng and Ziyong Ma and Tianyi Zhang and Matthew Johnson-Roberson and Weiming Zhi},
  booktitle = {RA-L 2026},
  year = {2026}
}