  Hybrid dynamical systems are ubiquitous as practical robotic applications
often involve both continuous states and discrete switchings. Safety is a
primary concern for hybrid robotic systems. Existing safety-critical control
approaches for hybrid systems are either computationally inefficient,
detrimental to system performance, or limited to small-scale systems. To amend
these drawbacks, in this paper, we propose a learningenabled approach to
construct local Control Barrier Functions (CBFs) to guarantee the safety of a
wide class of nonlinear hybrid dynamical systems. The end result is a safe
neural CBFbased switching controller. Our approach is computationally
efficient, minimally invasive to any reference controller, and applicable to
large-scale systems. We empirically evaluate our framework and demonstrate its
efficacy and flexibility through two robotic examples including a
high-dimensional autonomous racing case, against other CBF-based approaches and
model predictive control.
