Safety assurance is critical in the planning and control of robotic systems. For robots operating in the real world, the safety-critical design often needs to explicitly address uncertainties and the pre-computed guarantees often rely on the assumption of the particular distribution of the uncertainty. However, it is difficult to characterize the actual uncertainty distribution beforehand and thus the established safety guarantee may be violated due to possible distribution mismatch. In this paper, we propose a novel safe control framework that provides a high-probability safety guarantee for stochastic dynamical systems following unknown distributions of motion noise. Specifically, this framework adopts adaptive conformal prediction to dynamically quantify the prediction uncertainty from online observations and combines that with the probabilistic extension of the control barrier functions (CBFs) to characterize the uncertainty-aware control constraints. By integrating the constraints in the model predictive control scheme, it allows robots to adaptively capture the true prediction uncertainty online in a distribution-free setting and enjoys formally provable high-probability safety assurance. Simulation results on multi-robot systems with stochastic single-integrator dynamics and unicycle dynamics are provided to demonstrate the effectiveness of our framework.
Quantification of Actual Road User Behavior on the Basis of Given Traffic Rules
FZI Research Center for Information Technology（FZI信息技术研究中心）
道路上的驾驶受到各种交通规则的限制，旨在确保所有交通参与者的安全。然而，人类道路使用者通常不严格遵守这些规则，导致不同程度的规则符合性。这种对既定规则的偏离是当今道路交通的关键组成部分。在自动驾驶中，当不考虑规则偏差时，机器人代理可能会扰乱交通流量。在本文中，我们提出了一种方法来获得的规则符合度的分布，从人类驾驶数据。我们使用Waymo Open Motion数据集和安全距离和速度限制规则来演示我们的方法。
Safe Probabilistic Planning for Human-Robot Interaction using Conformal Risk Control
University of Washington, Department of Electrical and Computer Engineering（华盛顿大学电气与计算机工程系）；University of Washington, Department of Aeronautics and Astronautics（华盛顿大学航空与航天系）
Adaptive Safety Filtering for Frozen ACC Policies via Conformal Residual Calibration
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Wuhan University of Technology（武汉理工大学）；Shanghai Jiao Tong University（上海交通大学）
Conformalized Interactive Imitation Learning: Handling Expert Shift and Intermittent Feedback
Robotics Institute, School of Computer Science, Carnegie Mellon University（机器人研究所、计算机科学学院、卡内基梅隆大学）；Departments of Statistics and Machine Learning, Carnegie Mellon University（统计学与机器学习系、卡内基梅隆大学）
