[2506.05171] Towards provable probabilistic safety for scalable embodied AI systems

Electrical Engineering and Systems Science > Systems and Control arXiv:2506.05171 (eess) [Submitted on 5 Jun 2025 (v1), last revised 8 Apr 2026 (this version, v3)] Title:Towards provable probabilistic safety for scalable embodied AI systems Authors:Linxuan He, Lingxiang Fan, Qing-Shan Jia, Ang Li, Hongyan Sang, Ling Wang, Guanghui Wen, Jiwen Lu, Tao Zhang, Jie Zhou, Yi Zhang, Yisen Wang, Peng Wei, Zhongyuan Wang, Henry X. Liu, Shuo Feng View a PDF of the paper titled Towards provable probabilistic safety for scalable embodied AI systems, by Linxuan He and 15 other authors View PDF HTML (experimental) Abstract:Embodied AI systems, comprising AI models and physical plants, are increasingly prevalent across various applications. Due to the rarity of system failures, ensuring their safety in complex operating environments remains a major challenge, which severely hinders their large-scale deployment in safety-critical domains, such as autonomous vehicles, medical devices, and robotics. While achieving provable deterministic safety-verifying system safety across all possible scenarios-remains theoretically ideal, the rarity and complexity of corner cases make this approach impractical for scalable embodied AI systems. Instead, empirical safety evaluation is employed as an alternative, but the absence of provable guarantees imposes significant limitations. To address these issues, we argue for a paradigm shift to provable probabilistic safety that integrates provable guarantees ...