做交互式机器人安全验证的团队终于有了兼顾宽松性和形式化保证的方法——BeliefSF结合共形预测，在减少保守性的同时保持样本效率，做自动驾驶或人机协作的开发者值得关注。
该论文提出了一种基于共形预测的算法，用于验证交互式机器人中信念空间安全过滤器（BeliefSF）的高概率安全性。传统安全过滤器仅考虑物理空间，而BeliefSF在运行时结合推理主动降低机器人对人行为的不确定性，从而减少过滤的保守性。然而，由于运行时推理误差和神经网络近似的高维性，提供形式化安全保证极具挑战。作者通过聚焦于推理可靠区域进行验证，保留了共形预测的简单性和样本复杂度，同时显著降低了安全过滤器的保守性。在模拟人车交互基准测试中，该方法比标准共形预测基线验证了更宽松的安全过滤器。
Permissive Safety Through Trusted Inference: Verifiable Belief-Space Neural Safety Filters for Assured Interactive Robotics
Autonomous robots that interact with people must make safe and efficient decisions under human-induced uncertainty, such as their preferences, goals, competency, and willingness to cooperate. Safety filters are a popular approach for ensuring safety in interactive robotics, since their modular design separates safety from performance, allowing robots to operate safely around people with minimal impact on task efficiency. While traditional safety filters typically operate only in the physical space, neglecting the robot's ability to learn and adapt online, the recently proposed belief-space safety filter (BeliefSF) reasons about robot safety in closed-loop with runtime inference that actively reduces the robot's uncertainty online, thereby reducing conservativeness in filtering. However, providing formal safety guarantees for robots deploying BeliefSF remains a significant challenge due to errors in runtime inference and neural approximation of safety filters required to handle the high dimensionality of belief spaces. In this paper, we propose an algorithmic approach to certify high-probability safety of BeliefSF using conformal prediction, while explicitly accounting for the reliability of the robot's runtime inference module. Our method leverages the structure of belief-space safety filtering by focusing verification on a region where inference is expected to be reliable. It preserves the simplicity and sample complexity of standard conformal prediction, yet can certify a substantially less conservative safety filter. Through a simulated human-vehicle interaction benchmark, we show that our approach verifies a significantly more permissive belief-space safety filter than a standard conformal prediction baseline.