CPED-NCBFs: A Conformal Prediction for Expert Demonstration-based Neural Control Barrier Functions
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908457306685440 |
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| author | MS, Sumeadh Dsouza, Kevin Prakash, Ravi |
| author_facet | MS, Sumeadh Dsouza, Kevin Prakash, Ravi |
| contents | Among the promising approaches to enforce safety in control systems, learning Control Barrier Functions (CBFs) from expert demonstrations has emerged as an effective strategy. However, a critical challenge remains: verifying that the learned CBFs truly enforce safety across the entire state space. This is especially difficult when CBF is represented using neural networks (NCBFs). Several existing verification techniques attempt to address this problem including SMT-based solvers, mixed-integer programming (MIP), and interval or bound-propagation methods but these approaches often introduce loose, conservative bounds. To overcome these limitations, in this work we use CPED-NCBFs a split-conformal prediction based verification strategy to verify the learned NCBF from the expert demonstrations. We further validate our method on point mass systems and unicycle models to demonstrate the effectiveness of the proposed theory. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15022 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CPED-NCBFs: A Conformal Prediction for Expert Demonstration-based Neural Control Barrier Functions MS, Sumeadh Dsouza, Kevin Prakash, Ravi Robotics Systems and Control Among the promising approaches to enforce safety in control systems, learning Control Barrier Functions (CBFs) from expert demonstrations has emerged as an effective strategy. However, a critical challenge remains: verifying that the learned CBFs truly enforce safety across the entire state space. This is especially difficult when CBF is represented using neural networks (NCBFs). Several existing verification techniques attempt to address this problem including SMT-based solvers, mixed-integer programming (MIP), and interval or bound-propagation methods but these approaches often introduce loose, conservative bounds. To overcome these limitations, in this work we use CPED-NCBFs a split-conformal prediction based verification strategy to verify the learned NCBF from the expert demonstrations. We further validate our method on point mass systems and unicycle models to demonstrate the effectiveness of the proposed theory. |
| title | CPED-NCBFs: A Conformal Prediction for Expert Demonstration-based Neural Control Barrier Functions |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2507.15022 |