Legged locomotion has recently achieved remarkable success with the progress
of machine learning techniques, especially deep reinforcement learning (RL).
Controllers employing neural networks have demonstrated empirical and
qualitative robustness against real-world uncertainties, including sensor noise
and external perturbations. However, formally investigating the vulnerabilities
of these locomotion controllers remains a challenge. This difficulty arises
from the requirement to pinpoint vulnerabilities across a long-tailed
distribution within a high-dimensional, temporally sequential space. As a first
step towards quantitative verification, we propose a computational method that
leverages sequential adversarial attacks to identify weaknesses in learned
locomotion controllers. Our research demonstrates that, even state-of-the-art
robust controllers can fail significantly under well-designed, low-magnitude
adversarial sequence. Through experiments in simulation and on the real robot,
we validate our approach's effectiveness, and we illustrate how the results it
generates can be used to robustify the original policy and offer valuable
insights into the safety of these black-box policies.

通过串行对抗攻击来识别学习行动控制器的弱点，验证了即使是最先进的鲁棒控制器也可能在设计良好、低幅度的对抗序列下显著失败，并展示了该方法生成的结果如何用于加强原始策略并深入了解这些黑盒策略的安全性。