Vision-Based Safe Human-Robot Collaboration with Uncertainty Guarantees
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866909045377466368 |
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| author | Thumm, Jakob Frei, Marian Ni, Tianle Althoff, Matthias Pavone, Marco |
| author_facet | Thumm, Jakob Frei, Marian Ni, Tianle Althoff, Matthias Pavone, Marco |
| contents | We propose a framework for vision-based human pose estimation and motion prediction that gives conformal prediction guarantees for certifiably safe human-robot collaboration. Our framework combines aleatoric uncertainty estimation with OOD detection for high probabilistic confidence. To integrate our pipeline in certifiable safety frameworks, we propose conformal prediction sets for human motion predictions with high, valid confidence. We evaluate our pipeline on recorded human motion data and a real-world human-robot collaboration setting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_15221 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Vision-Based Safe Human-Robot Collaboration with Uncertainty Guarantees Thumm, Jakob Frei, Marian Ni, Tianle Althoff, Matthias Pavone, Marco Robotics Computer Vision and Pattern Recognition We propose a framework for vision-based human pose estimation and motion prediction that gives conformal prediction guarantees for certifiably safe human-robot collaboration. Our framework combines aleatoric uncertainty estimation with OOD detection for high probabilistic confidence. To integrate our pipeline in certifiable safety frameworks, we propose conformal prediction sets for human motion predictions with high, valid confidence. We evaluate our pipeline on recorded human motion data and a real-world human-robot collaboration setting. |
| title | Vision-Based Safe Human-Robot Collaboration with Uncertainty Guarantees |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.15221 |