Learning Conservative Neural Control Barrier Functions from Offline Data
This addresses safety-critical control problems in robotics and autonomous systems, offering an incremental improvement over existing deep learning approaches for control barrier functions.
The paper tackles the curse-of-dimensionality in synthesizing safety filters for dynamical systems by proposing an algorithm to train neural control barrier functions from offline data, which prevents unsafe states and disincentivizes out-of-distribution states. Empirical results show that these Conservative Control Barrier Functions (CCBFs) outperform existing methods in maintaining safety with minimal impact on task performance.
Safety filters, particularly those based on control barrier functions, have gained increased interest as effective tools for safe control of dynamical systems. Existing correct-by-construction synthesis algorithms for such filters, however, suffer from the curse-of-dimensionality. Deep learning approaches have been proposed in recent years to address this challenge. In this paper, we add to this set of approaches an algorithm for training neural control barrier functions from offline datasets. Such functions can be used to design constraints for quadratic programs that are then used as safety filters. Our algorithm trains these functions so that the system is not only prevented from reaching unsafe states but is also disincentivized from reaching out-of-distribution ones, at which they would be less reliable. It is inspired by Conservative Q-learning, an offline reinforcement learning algorithm. We call its outputs Conservative Control Barrier Functions (CCBFs). Our empirical results demonstrate that CCBFs outperform existing methods in maintaining safety while minimally affecting task performance. Source code is available at https://github.com/tabz23/CCBF.