PMP boundary sampling improves data efficiency for learning predictive safety filters via Hamilton-Jacobi reachability, shown in simulations and automotive racing experiments.
Advances in the theory of control barrier functions: Addressing practical challenges in safe control synthesis for autonomous and robotic systems
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Boundary Sampling to Learn Predictive Safety Filters via Pontryagin's Maximum Principle
PMP boundary sampling improves data efficiency for learning predictive safety filters via Hamilton-Jacobi reachability, shown in simulations and automotive racing experiments.