Safety on the Fly: Constructing Robust Safety Filters via Policy Control Barrier Functions at Runtime
Luzia Knoedler, Oswin So, Ji Yin, Mitchell Black, Zachary Serlin, Panagiotis Tsiotras, Javier Alonso-Mora, Chuchu Fan
AI summary
Problem
Constructing valid Control Barrier Functions for high relative-degree systems with input constraints is difficult, and existing robust methods rely on accurate models or offline training, making them sensitive to real-world disturbances.
Approach
The authors introduce Robust Policy Control Barrier Functions (RPCBF), which approximate the robust value function at runtime by sampling bounded disturbance trajectories and evaluating a nominal policy over a finite horizon, using cubic splines to accurately compute constraint violations and gradients.
Key results
- Runtime CBF construction via finite-horizon policy evaluation without offline training
- Theoretical conditions guaranteeing the finite-horizon approximation as a valid CBF
- Cubic spline-based time discretization eliminating gradient errors from naive approximations
- Real-time collision avoidance on a hardware quadcopter under model errors
Why it matters
It provides a practical, model-agnostic safety guarantee for autonomous systems operating under real-world uncertainties, benefiting robotics and autonomous control researchers and practitioners.
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/