Safety on the Fly: Constructing Robust Safety Filters via Policy Control Barrier Functions at Runtime

TL;DR

This paper introduces the Robust Policy CBF (RPCBF), an online method for constructing safety filters for nonlinear systems that accounts for disturbances and input constraints, demonstrated through simulations and hardware experiments.

Contribution

The paper presents a novel online approach to approximate Control Barrier Functions using value function estimation, enhancing safety guarantees in high relative degree systems.

Findings

RPCBF effectively guarantees safety in simulations.

The method compensates for model errors on hardware.

RPCBF outperforms existing safety filters in robustness.

Abstract

Control Barrier Functions (CBFs) have proven to be an effective tool for performing safe control synthesis for nonlinear systems. However, guaranteeing safety in the presence of disturbances and input constraints for high relative degree systems is a difficult problem. In this work, we propose the Robust Policy CBF (RPCBF), a practical approach for constructing robust CBF approximations online via the estimation of a value function. We establish conditions under which the approximation qualifies as a valid CBF and demonstrate the effectiveness of the RPCBF-safety filter in simulation on a variety of high relative degree input-constrained systems. Finally, we demonstrate the benefits of our method in compensating for model errors on a hardware quadcopter platform by treating the model errors as disturbances. Website including code: www.oswinso.xyz/rpcbf/