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.

翻译：与人类交互的自主机器人必须在人类引发的不确定性（如偏好、目标、能力及合作意愿）下做出安全高效的决策。安全滤波器是确保交互式机器人安全性的常用方法，其模块化设计将安全性与性能分离，使机器人能在最小化任务效率影响的前提下安全地与人共处。传统安全滤波器通常仅在物理空间中运作，忽视了机器人在线学习与自适应能力，而近期提出的信念空间安全滤波器（BeliefSF）则通过与运行时推理形成闭环来推演机器人安全性，该推理能主动降低机器人的在线不确定性，从而减少滤波的保守性。然而，由于运行时推理误差以及处理信念空间高维性所需的安全滤波器神经逼近误差，为部署BeliefSF的机器人提供形式化安全保证仍是一项重大挑战。本文提出一种算法方法，利用共形预测对BeliefSF进行高概率安全性认证，并明确考虑机器人运行时推理模块的可靠性。我们的方法利用信念空间安全滤波的结构特性，将验证聚焦于推断可信的区域。该方法既保持了标准共形预测的简洁性与样本复杂度，又能认证一个显著降低保守性的安全滤波器。通过一个模拟人-车交互基准测试，我们证明该方法验证的信念空间安全滤波器比标准共形预测基线具有显著更高的容许性。