用投注法提升仿真到现实的性能评估可靠性，实现随时有效的置信序列。
Sim-to-Real Betting on the E-Process: Bringing "simulators" to anytime-valid confidence sequences
- 融合仿真性能估计与投注机制，构建动态置信序列。
- 在机器人测试中实现高效可靠的均值估计验证。
- 适合需要实时安全推断的自主系统性能评估场景。
本文将仿真到现实的性能估计算法与投注方法（Chen et al.）及安全的任意时间有效推断（Ramdas et al.）相结合，利用缩放后的仿真器，生成高效的均值估计可信证书。该方法在机器人性能测试中尤为有价值，能提供随时有效的置信序列。本文给出方法的完整自包含描述，相关预备知识尽量简化，具体细节请参考原论文。部分合成实验示例见 https://github.com/ISUSAIL/Bet4Sim2Real-EProcess。
原文摘要 · Abstract (English)
This note describes an integration of the sim-to-real performance estimate with betting (from Chen et al.) and the safe anytime-valid inference (from Ramdas et al.). Using the scaled simulators. The method produces efficient, reliable certificates for the mean estimate, an approach that is especially valuable in robot performance testing. This note gives a primary, self-contained account of the construction; preliminaries of the respective methods are kept at a minimum, and one shall refer to the original works for full detail. Some synthetic examples demonstrating the proposed algorithm can be found at https://github.com/ISUSAIL/Bet4Sim2Real-EProcess.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。