Domain Adaptive Safety Filters via Deep Operator Learning
This addresses the need for adaptable safety filters in control systems, though it appears incremental as it builds on existing CBF methods.
The paper tackled the problem of limited adaptability in learning-based Control Barrier Functions (CBFs) for safety-critical control by proposing a self-supervised deep operator learning framework that maps environmental parameters to CBFs, demonstrating effectiveness in navigation tasks with dynamic obstacles.
Learning-based approaches for constructing Control Barrier Functions (CBFs) are increasingly being explored for safety-critical control systems. However, these methods typically require complete retraining when applied to unseen environments, limiting their adaptability. To address this, we propose a self-supervised deep operator learning framework that learns the mapping from environmental parameters to the corresponding CBF, rather than learning the CBF directly. Our approach leverages the residual of a parametric Partial Differential Equation (PDE), where the solution defines a parametric CBF approximating the maximal control invariant set. This framework accommodates complex safety constraints, higher relative degrees, and actuation limits. We demonstrate the effectiveness of the method through numerical experiments on navigation tasks involving dynamic obstacles.