标题： 首先，了解你所不知道的：操控极限下主动信息收集的驾驶方法 显示英文标题

标题： First, Learn What You Don't Know: Active Information Gathering for Driving at the Limits of Handling

摘要： 结合能够在线适应的数据驱动模型和模型预测控制（MPC）已实现了对非线性系统的有效控制。然而，在不稳定系统上部署时，在线适应可能不够快，无法确保可靠的同时学习与控制。例如，执行高度动态操作的车辆控制器可能会将轮胎推至摩擦极限，使车辆失去稳定性，并让建模误差迅速累积导致失控。在这项工作中，我们提出了一种贝叶斯元学习MPC框架。我们设计了一个表达能力强的车辆动力学模型，利用贝叶斯最后一层元学习实现快速在线适应。该模型的不确定性估计被用于指导信息丰富的数据收集，并在部署前快速改进模型。在丰田Supra上的实验表明，(i) 该框架能够在动态漂移操作中实现可靠的控制，(ii) 单纯的在线适应可能不足以实现接近稳定极限车辆的零样本控制，以及(iii) 主动数据收集有助于实现可靠性能。 显示英文摘要

摘要： Combining data-driven models that adapt online and model predictive control (MPC) has enabled effective control of nonlinear systems. However, when deployed on unstable systems, online adaptation may not be fast enough to ensure reliable simultaneous learning and control. For example, controllers on a vehicle executing highly dynamic maneuvers may push the tires to their friction limits, destabilizing the vehicle and allowing modeling errors to quickly compound and cause a loss of control. In this work, we present a Bayesian meta-learning MPC framework. We propose an expressive vehicle dynamics model that leverages Bayesian last-layer meta-learning to enable rapid online adaptation. The model's uncertainty estimates are used to guide informative data collection and quickly improve the model prior to deployment. Experiments on a Toyota Supra show that (i) the framework enables reliable control in dynamic drifting maneuvers, (ii) online adaptation alone may not suffice for zero-shot control of a vehicle at the edge of stability, and (iii) active data collection helps achieve reliable performance.