Multistep Belief Space Dynamics Learning For Risk-Aware Control

[2605.12628] Multistep Belief Space Dynamics Learning For Risk-Aware Control

[Submitted on 12 May 2026]

Title:Multistep Belief Space Dynamics Learning For Risk-Aware Control

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Abstract:As autonomous vehicles move from a simplified research setting to practical use, there exists a large gap between the dynamic behavior of a human driving and an autonomous system. Risk-aware behavior needs to naturally develop in order to scale to the demands of the real world. A major issue for risk-aware planning and control has been predicting how dynamical uncertainty evolves through time and optimizing plans that account for this without being overly conservative. Here, we present a learning framework to predict distributional dynamics that can be optimized in real time for Model Predictive Control (MPC). We explore the importance of structure when learning distributional dynamics for use in MPC. A rigorous ablation study is conducted on a large dataset of real world off-road driving that shows the impact of deviations from our proposed structure. Furthermore, we deploy our learned model and planning stack on a full sized vehicle in challenging off-road conditions. Our planning architecture is able to naturally regulate the speed of the vehicle based on the environment and consistently demonstrates intelligent behavior over miles of diverse terrain.

Subjects:

Robotics (cs.RO)

Cite as: arXiv:2605.12628 [cs.RO]

(or arXiv:2605.12628v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2605.12628

arXiv-issued DOI via DataCite

Submission history

From: Jason Gibson [view email] [v1] Tue, 12 May 2026 18:11:12 UTC (25,600 KB)

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