  Reinforcement learning is a powerful technique for developing new robot
behaviors. However, typical lack of safety guarantees constitutes a hurdle for
its practical application on real robots. To address this issue, safe
reinforcement learning aims to incorporate safety considerations, enabling
faster transfer to real robots and facilitating lifelong learning. One
promising approach within safe reinforcement learning is the use of control
barrier functions. These functions provide a framework to ensure that the
system remains in a safe state during the learning process. However,
synthesizing control barrier functions is not straightforward and often
requires ample domain knowledge. This challenge motivates the exploration of
data-driven methods for automatically defining control barrier functions, which
is highly appealing. We conduct a comprehensive review of the existing
literature on safe reinforcement learning using control barrier functions.
Additionally, we investigate various techniques for automatically learning the
Control Barrier Functions, aiming to enhance the safety and efficacy of
Reinforcement Learning in practical robot applications.
