AURORA: Active Uncertainty-Driven Re-Orientation for In-Hand Reconstruction

Abstract:Observing objects grasped by a robot hand is challenging due to severe visual occlusions. Although in-hand manipulation can expose hidden surfaces, existing approaches often rely on predefined or open-loop reorientation strategies that do not explicitly target under-observed regions. We propose AURORA, an active 3D reconstruction framework that closes the loop between online object-centric reconstruction and in-hand reorientation. At its core, Ray-GPIS estimates direction-wise reconstruction uncertainty along candidate viewing rays and selects next-best-view targets using an uncertainty--novelty objective, which are realized through an axis-conditioned in-hand rotation policy. The resulting RGB-D observations are fused incrementally using CAD-free 6D pose tracking and lightweight geometric reconstruction. Experiments demonstrate that AURORA improves reconstruction quality and information-acquisition efficiency over non-active rotation strategies, while Ray-GPIS also outperforms active view-planning baselines in reconstruction performance, action-ranking quality, and planning efficiency. Targeted ablations further validate its robustness to hand occlusion and pose errors. The project webpage is available at this https URL

23 pages, 11 figures, 6 tables. Accepted to the 10th Conference on Robot Learning (CoRL 2026)

Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2609.08493 [cs.RO]

(or arXiv:2609.08493v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.08493 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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