Publication
SE(3)-PoseFlow: Estimating 6D Pose Distributions for Uncertainty-Aware Robotic Manipulation
Yufeng Jin; Niklas Funk; Vignesh Prasad; Zechu Li; Mathias Franzius; Jan Peters; Georgia Chalvatzaki
In: IEEE International Conference on Robotics and Automation, ICRA 2026, Vienna, Austria, June 1-5, 2026. IEEE International Conference on Robotics and Automation (ICRA), Pages 9511-9518, IEEE, 2026.
Abstract
Object pose estimation is a fundamental problem
in robotics and computer vision, yet it remains challenging
due to partial observability, occlusions, and object symmetries,
which inevitably lead to pose ambiguity and multiple hypotheses
consistent with the same observation. While deterministic
deep networks achieve impressive performance under well-
constrained conditions, they are often overconfident and fail to
capture the multi-modality of the underlying pose distribution.
To address these challenges, we propose a novel probabilistic
framework that leverages flow matching on the SE(3) manifold
for estimating 6D object pose distributions. Unlike existing
methods that regress a single deterministic output, our ap-
proach models the full pose distribution with a sample-based
estimate and enables reasoning about uncertainty in ambiguous
cases such as symmetric objects or severe occlusions. We
achieve state-of-the-art results on Real275, YCB-V and LM-
O, and demonstrate how our sample-based pose estimates can
be leveraged in downstream robotic manipulation tasks such
as active perception for disambiguating uncertain viewpoints,
or guiding grasp synthesis in an uncertainty-aware manner.