State-Dependent Conformal Perception Bounds for Neuro-Symbolic Verification of Autonomous Systems
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915292302540800 |
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| author | Waite, Thomas Geng, Yuang Turnquist, Trevor Ruchkin, Ivan Ivanov, Radoslav |
| author_facet | Waite, Thomas Geng, Yuang Turnquist, Trevor Ruchkin, Ivan Ivanov, Radoslav |
| contents | It remains a challenge to provide safety guarantees for autonomous systems with neural perception and control. A typical approach obtains symbolic bounds on perception error (e.g., using conformal prediction) and performs verification under these bounds. However, these bounds can lead to drastic conservatism in the resulting end-to-end safety guarantee. This paper proposes an approach to synthesize symbolic perception error bounds that serve as an optimal interface between perception performance and control verification. The key idea is to consider our error bounds to be heteroskedastic with respect to the system's state -- not time like in previous approaches. These bounds can be obtained with two gradient-free optimization algorithms. We demonstrate that our bounds lead to tighter safety guarantees than the state-of-the-art in a case study on a mountain car. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_21308 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | State-Dependent Conformal Perception Bounds for Neuro-Symbolic Verification of Autonomous Systems Waite, Thomas Geng, Yuang Turnquist, Trevor Ruchkin, Ivan Ivanov, Radoslav Systems and Control It remains a challenge to provide safety guarantees for autonomous systems with neural perception and control. A typical approach obtains symbolic bounds on perception error (e.g., using conformal prediction) and performs verification under these bounds. However, these bounds can lead to drastic conservatism in the resulting end-to-end safety guarantee. This paper proposes an approach to synthesize symbolic perception error bounds that serve as an optimal interface between perception performance and control verification. The key idea is to consider our error bounds to be heteroskedastic with respect to the system's state -- not time like in previous approaches. These bounds can be obtained with two gradient-free optimization algorithms. We demonstrate that our bounds lead to tighter safety guarantees than the state-of-the-art in a case study on a mountain car. |
| title | State-Dependent Conformal Perception Bounds for Neuro-Symbolic Verification of Autonomous Systems |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2502.21308 |