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Control barrier functions (CBFs) have become a popular tool to enforce safetyof a control system. CBFs are commonly utilized in a quadratic programformulation (CBF-QP) as safety-critical constraints. A class $\mathcal{K}$function in CBFs usually needs to be tuned manually in order to balance thetrade-off between performance and safety for each environment. However, thisprocess is often heuristic and can become intractable for high relative-degreesystems. Moreover, it prevents the CBF-QP from generalizing to differentenvironments in the real world. By embedding the optimization procedure of theexponential control barrier function based quadratic program (ECBF-QP) as adifferentiable layer within a deep learning architecture, we propose adifferentiable safety-critical control framework that enables generalization tonew environments for high relative-degree systems with forward invarianceguarantees. Finally, we validate the proposed control design with 2D double andquadruple integrator systems in various environments.

Learning Differentiable Safety-Critical Control using Control Barrier Functions for Generalization to Novel Environments