  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.

Robotics;Machine Learning;Systems and Control;Systems and Control