From Space to Time: Enabling Adaptive Safety with Learned Value Functions via Disturbance Recasting

Sander Tonkens, Nikhil Uday Shinde, Azra Begzadić, Michael C. Yip, Jorge Cortes, Sylvia Lee Herbert

Keywords: Disturbance-aware Safety, Reachability Analysis

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.

Submission Number: 505