  The robustness of legged locomotion is crucial for quadrupedal robots in
challenging terrains. Recently, Reinforcement Learning (RL) has shown promising
results in legged locomotion and various methods try to integrate privileged
distillation, scene modeling, and external sensors to improve the
generalization and robustness of locomotion policies. However, these methods
are hard to handle uncertain scenarios such as abrupt terrain changes or
unexpected external forces. In this paper, we consider a novel risk-sensitive
perspective to enhance the robustness of legged locomotion. Specifically, we
employ a distributional value function learned by quantile regression to model
the aleatoric uncertainty of environments, and perform risk-averse policy
learning by optimizing the worst-case scenarios via a risk distortion measure.
Extensive experiments in both simulation environments and a real Aliengo robot
demonstrate that our method is efficient in handling various external
disturbances, and the resulting policy exhibits improved robustness in harsh
and uncertain situations in legged locomotion. Videos are available at
https://risk-averse-locomotion.github.io/.
