标题： 首先，学习你不知道的东西：在操控极限下驾驶的主动信息收集 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： 将在线自适应的数据驱动模型与模型预测控制（MPC）相结合，已实现了对非线性系统的有效控制。然而，当部署在不稳定系统上时，在线自适应可能不够快，无法确保可靠的同时学习和控制。例如，执行高度动态操作（如漂移以避开障碍物）的车辆控制器可能会将车辆轮胎推向摩擦极限，导致车辆失稳，并使建模误差迅速累积，从而导致失控。为解决这一挑战，我们提出了一种主动信息收集框架，以尽可能快速地识别车辆动力学。我们提出了一种表达能力强的车辆动力学模型，该模型利用贝叶斯最后一层元学习来实现快速在线自适应。该模型的不确定性估计用于指导有信息量的数据收集，并在部署前快速改进模型。在丰田Supra上的动态漂移实验表明，(i) 该框架能够在稳定性的边缘实现车辆的可靠控制，(ii) 仅依靠在线自适应可能不足以实现零样本控制，并可能导致不良的瞬态误差或甩尾，(iii) 主动数据收集有助于实现可靠的性能。 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, a controller on a vehicle executing highly dynamic maneuvers--such as drifting to avoid an obstacle--may push the vehicle's tires to their friction limits, destabilizing the vehicle and allowing modeling errors to quickly compound and cause a loss of control. To address this challenge, we present an active information gathering framework for identifying vehicle dynamics as quickly as possible. 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. Dynamic drifting experiments on a Toyota Supra show that (i) the framework enables reliable control of a vehicle at the edge of stability, (ii) online adaptation alone may not suffice for zero-shot control and can lead to undesirable transient errors or spin-outs, and (iii) active data collection helps achieve reliable performance.