Domain Adaptive Safety Filters via Deep Operator Learning
Fuente:
arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866912076227674112 |
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| author | Manda, Lakshmideepakreddy Chen, Shaoru Fazlyab, Mahyar |
| author_facet | Manda, Lakshmideepakreddy Chen, Shaoru Fazlyab, Mahyar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14528 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Domain Adaptive Safety Filters via Deep Operator Learning Manda, Lakshmideepakreddy Chen, Shaoru Fazlyab, Mahyar Systems and Control Machine Learning Robotics 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. |
| title | Domain Adaptive Safety Filters via Deep Operator Learning |
| topic | Systems and Control Machine Learning Robotics |
| url | https://arxiv.org/abs/2410.14528 |