  Humans have the ability to deviate from their natural behavior when
necessary, which is a cognitive process called response inhibition. Similar
approaches have independently received increasing attention in recent years for
ensuring the safety of control. Realized using control barrier functions or
predictive safety filters, these approaches can effectively ensure the
satisfaction of state constraints through an online adaptation of nominal
control laws, e.g., obtained through reinforcement learning. While the focus of
these realizations of inhibitory control has been on risk-neutral formulations,
human studies have shown a tight link between response inhibition and risk
attitude. Inspired by this insight, we propose a flexible, risk-sensitive
method for inhibitory control. Our method is based on a risk-aware condition
for value functions, which guarantees the satisfaction of state constraints. We
propose a method for learning these value functions using common techniques
from reinforcement learning and derive sufficient conditions for its success.
By enforcing the derived safety conditions online using the learned value
function, risk-sensitive inhibitory control is effectively achieved. The
effectiveness of the developed control scheme is demonstrated in simulations.
