Learning Neural Control Barrier Functions from Expert Demonstrations using Inverse Constraint Learning
This addresses safety for autonomous systems in critical domains like driving, but it is incremental as it builds on existing neural CBF methods with a new data-labeling approach.
The paper tackles the problem of learning neural control barrier functions (CBFs) for safety in autonomous systems when failure sets are hard to specify, by using inverse constraint learning from expert demonstrations to train a constraint function and then a neural CBF, achieving comparable performance to ground-truth labeled data in four environments.
Safety is a fundamental requirement for autonomous systems operating in critical domains. Control barrier functions (CBFs) have been used to design safety filters that minimally alter nominal controls for such systems to maintain their safety. Learning neural CBFs has been proposed as a data-driven alternative for their computationally expensive optimization-based synthesis. However, it is often the case that the failure set of states that should be avoided is non-obvious or hard to specify formally, e.g., tailgating in autonomous driving, while a set of expert demonstrations that achieve the task and avoid the failure set is easier to generate. We use ICL to train a constraint function that classifies the states of the system under consideration to safe, i.e., belong to a controlled forward invariant set that is disjoint from the unspecified failure set, and unsafe ones, i.e., belong to the complement of that set. We then use that function to label a new set of simulated trajectories to train our neural CBF. We empirically evaluate our approach in four different environments, demonstrating that it outperforms existing baselines and achieves comparable performance to a neural CBF trained with the same data but annotated with ground-truth safety labels.