Learning Local Control Barrier Functions for Safety Control of Hybrid
  Systems
Shuo Yang Yu Chen Xiang Yin Rahul Mangharam
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.