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From space to time: enabling adaptive safety with learned value functions via disturbance recasting
S. Tonkens, N. U. Shinde, A. Begzadić, M. C. Yip, J. Cortés, S. L. Herbert
Conference on Robot Learning, Seoul, Korea, 2025, Proceedings of Machine Learning Research, volume 305, pp. 4103-4122

Abstract

Safe operation is essential for autonomous systems in safety-critical environments such as urban air mobility. Value function-based safety filters provide formal guarantees on safety, wrapping learned or planning-based controllers with a layer of protection. Recent approaches leverage offline learned value functions to scale these safety filters to high-dimensional systems. Yet these methods assume detailed prior knowledge of all possible sources of model mismatch, in the form of disturbances, in the environment -- information that is typically unavailable in real world settings. Even in well-mapped environments like urban canyons or industrial sites, drones encounter complex, spatially-varying disturbances arising from payload-drone interaction, turbulent airflow, and other environmental factors. We introduce Space2Time, which enables safe and adaptive deployment of offline-learned safety filters under unknown, spatially-varying disturbances. The key idea is to reparameterize spatial disturbances as a time-varying formulation, allowing the use of temporally varying precomputed value functions during online operation. We validate Space2Time through extensive simulations on diverse quadcopter models and real-world hardware experiments, demonstrating significantly improved safety performance over worst-case and naive baselines.

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Mechanical and Aerospace Engineering, University of California, San Diego
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