Learning Neural Control Barrier Functions from Expert Demonstrations using Inverse Constraint Learning

TL;DR

This paper introduces a method to learn neural control barrier functions from expert demonstrations using inverse constraint learning, enabling safety classification without explicitly specifying failure sets, and demonstrates its effectiveness across multiple environments.

Contribution

The paper proposes a novel approach combining inverse constraint learning with neural CBFs to infer safety sets from demonstrations, reducing the need for explicit failure set specification.

Findings

Outperforms existing baselines in safety classification tasks.

Achieves comparable performance to ground-truth labeled neural CBFs.

Effective across four different environments.

Abstract

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.