分布偏移下用于STL运行时验证的鲁棒共形预测
Robust Conformal Prediction for STL Runtime Verification under Distribution Shift
AI总结：
针对分布偏移下CPS的STL任务运行时故障预测难题，提出基于f散度上界与鲁棒共形预测的RPRV算法，量化相关参数关系并在Franka机械臂上验证了有效性。
摘要：
在模拟器中设计的信息物理系统（CPS）在现实世界中的表现会有所不同。因此，当它们部署到现实环境中后，我们希望能在运行时预测系统故障。我们针对通用随机信息物理系统（CPS）的信号时序逻辑（STL）任务，提出了鲁棒预测性运行时验证（RPRV）算法。\nRPRV问题面临若干挑战：（1）已部署CPS的行为数据可能不足；（2）预测模型基于设计阶段遇到的系统轨迹分布构建，即部署期间可能存在分布偏移。为应对这些挑战，我们假设已知部署时与设计时分布之间统计距离的上界（以f散度衡量），并采用基于鲁棒共形预测的技术。受文献[1]中研究结果的启发，我们构建了一种兼具准确性和可解释性的RPRV算法。我们使用轨迹预测模型估计运行时的系统行为，并通过鲁棒共形预测考虑分布偏移，从而获得概率保证。我们精确量化了校准数据、期望置信度与可容许分布偏移之间的关系。据我们所知，这是该场景下首批在分布偏移下具备统计有效性的算法。我们在NVIDIA Isaac仿真环境中的Franka机械臂上对算法进行了实证验证。
英文摘要：
Cyber-physical systems (CPS) designed in simulators behave differently in the real-world. Once they are deployed in the real-world, we would hence like to predict system failures during runtime. We propose robust predictive runtime verification (RPRV) algorithms under signal temporal logic (STL) tasks for general stochastic CPS. The RPRV problem faces several challenges: (1) there may not be sufficient data of the behavior of the deployed CPS, (2) predictive models are based on a distribution over system trajectories encountered during the design phase, i.e., there may be a distribution shift during deployment. To address these challenges, we assume to know an upper bound on the statistical distance (in terms of an f-divergence) between the distributions at deployment and design time, and we utilize techniques based on robust conformal prediction. Motivated by our results in [1], we construct an accurate and an interpretable RPRV algorithm. We use a trajectory prediction model to estimate the system behavior at runtime and robust conformal prediction to obtain probabilistic guarantees by accounting for distribution shifts. We precisely quantify the relationship between calibration data, desired confidence, and permissible distribution shift. To the best of our knowledge, these are the first statistically valid algorithms under distribution shift in this setting. We empirically validate our algorithms on a Franka manipulator within the NVIDIA Isaac sim environment.
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