Modern end-to-end learning systems can learn to explicitly infer control from perception. However, it is difficult to guarantee stability and robustness for these systems since they are often exposed to unstructured, high-dimensional, and complex observation spaces (e.g., autonomous driving from a stream of pixel inputs). We propose to leverage control Lyapunov functions (CLFs) to equip end-to-end vision-based policies with stability properties and introduce stability attention in CLFs (att-CLFs) to tackle environmental changes and improve learning flexibility. We also present an uncertainty propagation technique that is tightly integrated into att-CLFs. We demonstrate the effectiveness of att-CLFs via comparison with classical CLFs, model predictive control, and vanilla end-to-end learning in a photo-realistic simulator and on a real full-scale autonomous vehicle.

翻译：现代端到端学习系统能够从感知中显式推断控制信号。然而，由于这些系统常暴露于非结构化、高维且复杂的观测空间（例如，从像素输入流中进行自动驾驶），因此很难保证其稳定性和鲁棒性。我们提出利用控制李雅普诺夫函数（CLFs）为基于视觉的端到端策略赋予稳定性特性，并在CLFs中引入稳定性注意力（att-CLFs）以应对环境变化并提升学习灵活性。我们还提出了一种紧密集成于att-CLFs中的不确定性传播技术。通过在照片级真实感仿真器以及实际全尺寸自动驾驶车辆上，与经典CLFs、模型预测控制和普通端到端学习进行对比，我们验证了att-CLFs的有效性。