  The last half-decade has seen a steep rise in the number of contributions on
safe learning methods for real-world robotic deployments from both the control
and reinforcement learning communities. This article provides a concise but
holistic review of the recent advances made in using machine learning to
achieve safe decision making under uncertainties, with a focus on unifying the
language and frameworks used in control theory and reinforcement learning
research. Our review includes: learning-based control approaches that safely
improve performance by learning the uncertain dynamics, reinforcement learning
approaches that encourage safety or robustness, and methods that can formally
certify the safety of a learned control policy. As data- and learning-based
robot control methods continue to gain traction, researchers must understand
when and how to best leverage them in real-world scenarios where safety is
imperative, such as when operating in close proximity to humans. We highlight
some of the open challenges that will drive the field of robot learning in the
coming years, and emphasize the need for realistic physics-based benchmarks to
facilitate fair comparisons between control and reinforcement learning
approaches.
