Learning Robust Hybrid Control Barrier Functions for Uncertain Systems

TL;DR

This paper introduces a data-driven method to learn robust hybrid control barrier functions, enhancing safety and expanding the safe operating region for uncertain systems like a compass gait walker.

Contribution

It proposes a novel optimization-based approach for synthesizing robust hybrid control barrier functions from data, ensuring safety in uncertain systems.

Findings

Successfully expanded the safe region of a compass gait walker

Provided conditions for data to guarantee correctness of the learned functions

Demonstrated robustness against model uncertainties

Abstract

The need for robust control laws is especially important in safety-critical applications. We propose robust hybrid control barrier functions as a means to synthesize control laws that ensure robust safety. Based on this notion, we formulate an optimization problem for learning robust hybrid control barrier functions from data. We identify sufficient conditions on the data such that feasibility of the optimization problem ensures correctness of the learned robust hybrid control barrier functions. Our techniques allow us to safely expand the region of attraction of a compass gait walker that is subject to model uncertainty.