Domain Adaptive Safety Filters via Deep Operator Learning

• URL: http://arxiv.org/abs/2410.14528v1
• Date: Fri, 18 Oct 2024 15:10:55 GMT
• Title: Domain Adaptive Safety Filters via Deep Operator Learning
• Authors: Lakshmideepakreddy Manda, Shaoru Chen, Mahyar Fazlyab,
• Abstract summary: We propose a self-supervised deep operator learning framework that learns the mapping from environmental parameters to the corresponding CBF. We demonstrate the effectiveness of the method through numerical experiments on navigation tasks involving dynamic obstacles.
• Score: 5.62479170374811
• License:
• Abstract: 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.

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