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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.10392 |
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| _version_ | 1866915852223250432 |
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| author | Gonzales, Jake Mizuta, Kazuki Leung, Karen Ratliff, Lillian J. |
| author_facet | Gonzales, Jake Mizuta, Kazuki Leung, Karen Ratliff, Lillian J. |
| contents | In this paper, we present a novel probabilistic safe control framework for human-robot interaction that combines control barrier functions (CBFs) with conformal risk control to provide formal safety guarantees while considering complex human behavior. The approach uses conformal risk control to quantify and control the prediction errors in CBF safety values and establishes formal guarantees on the probability of constraint satisfaction during interaction. We introduce an algorithm that dynamically adjusts the safety margins produced by conformal risk control based on the current interaction context. Through experiments on human-robot navigation scenarios, we demonstrate that our approach significantly reduces collision rates and safety violations as compared to baseline methods while maintaining high success rates in goal-reaching tasks and efficient control. The code, simulations, and other supplementary material can be found on the project website: https://jakeagonzales.github.io/crc-cbf-website/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_10392 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Safe Probabilistic Planning for Human-Robot Interaction using Conformal Risk Control Gonzales, Jake Mizuta, Kazuki Leung, Karen Ratliff, Lillian J. Robotics Artificial Intelligence In this paper, we present a novel probabilistic safe control framework for human-robot interaction that combines control barrier functions (CBFs) with conformal risk control to provide formal safety guarantees while considering complex human behavior. The approach uses conformal risk control to quantify and control the prediction errors in CBF safety values and establishes formal guarantees on the probability of constraint satisfaction during interaction. We introduce an algorithm that dynamically adjusts the safety margins produced by conformal risk control based on the current interaction context. Through experiments on human-robot navigation scenarios, we demonstrate that our approach significantly reduces collision rates and safety violations as compared to baseline methods while maintaining high success rates in goal-reaching tasks and efficient control. The code, simulations, and other supplementary material can be found on the project website: https://jakeagonzales.github.io/crc-cbf-website/. |
| title | Safe Probabilistic Planning for Human-Robot Interaction using Conformal Risk Control |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2603.10392 |