Control-Ready Uncertainty for Trajectory Diffusion

Abstract:Diffusion models can represent complex, multimodal trajectory distributions, but extracting uncertainty from them typically requires costly Monte Carlo sampling. This limits their use in real-time control, where robots must rapidly assess risk and maintain safety margins. We introduce Score-Curvature for Online Precision Estimation (SCOPE), a lightweight module that augments diffusion trajectory models with control-ready uncertainty. SCOPE learns a structured precision matrix around each nominal trajectory by distilling score-curvature information and producing calibrated Gaussian tubes with low overhead and without repeated Monte Carlo sampling. These tubes provide per-timestep covariance estimates that can be used both as predicted occupancy for moving agents and as adaptive exploration guides for robot control. We evaluate SCOPE with mode-conditioned multimodal diffusion backbones in pedestrian forecasting, crowd navigation, Maze2D control, and real-world Franka Panda manipulation. Across these settings, SCOPE provides fast uncertainty estimation, which leads to better closed-loop performance. Project page: this https URL

Accepted at the Conference on Robot Learning (CoRL) 2026 as a Spotlight presentation. 39 pages, 12 figures

Robotics (cs.RO)

arXiv:2610.12431 [cs.RO]

(or arXiv:2610.12431v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.12431 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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