Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Differentiable Safety-Critical Control using Control Barrier
  Functions for Generalization to Novel Environments   Control barrier functions (CBFs) have become a popular tool to enforce safety
of a control system. CBFs are commonly utilized in a quadratic program
formulation (CBF-QP) as safety-critical constraints. A class $\mathcal{K}$
function in CBFs usually needs to be tuned manually in order to balance the
trade-off between performance and safety for each environment. However, this
process is often heuristic and can become intractable for high relative-degree
systems. Moreover, it prevents the CBF-QP from generalizing to different
environments in the real world. By embedding the optimization procedure of the
exponential control barrier function based quadratic program (ECBF-QP) as a
differentiable layer within a deep learning architecture, we propose a
differentiable safety-critical control framework that enables generalization to
new environments for high relative-degree systems with forward invariance
guarantees. Finally, we validate the proposed control design with 2D double and
quadruple integrator systems in various environments.

23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th