基于学习的NMPC自适应：使用并行化数字孪生的自动驾驶应用
Learning Based NMPC Adaptation for Autonomous Driving using Parallelized Digital Twin
AI总结：
提出基于可执行数字孪生的数据驱动NMPC在线自适应方法，通过无导数优化和域随机化实现安全参数探索，仅需10分钟微调9个参数，将Sim2Real差距从876倍降至1.033倍，跟踪性能提升75%。
摘要：
在这项工作中，我们关注将自动驾驶控制器从仿真迁移到现实世界（即Sim2Real）的挑战。我们提出了一种数据高效的方法，用于对可参数化控制架构进行在线即时自适应，从而在考虑模型不匹配等不确定性、环境变化和任务变化的同时，优化目标闭环性能。该方法的新颖之处在于利用可执行数字孪生（xDTs）实现的黑盒优化，通过无导数方法进行数据驱动的参数校准，以实时直接自适应控制器。xDTs通过域随机化增强鲁棒性，并允许安全的参数探索。所提方法只需与现实世界进行最少的交互，因为它将探索推向xDTs。我们通过现实世界实验验证了该方法，证明了其在10分钟内迁移和微调具有9个参数的NMPC的有效性。这消除了数小时的手动调优和漫长的机器学习训练及数据收集阶段的需求。我们的结果表明，在线自适应的NMPC直接补偿了Sim2Real差距，并避免了在仿真中的过拟合。重要的是，跟踪性能实现了75%的提升，Sim2Real差距从876倍降低到1.033倍。
英文摘要：
In this work, we focus on the challenge of transferring an autonomous driving controller from simulation to the real world (i.e. Sim2Real). We propose a data-efficient method for online and on-the-fly adaptation of parametrizable control architectures such that the target closed-loop performance is optimized while accounting for uncertainties as model mismatches, changes in the environment, and task variations. The novelty of the approach resides in leveraging black-box optimization enabled by Executable Digital Twins (xDTs) for data-driven parameter calibration through derivative-free methods to directly adapt the controller in real-time. The xDTs are augmented with Domain Randomization for robustness and allow for safe parameter exploration. The proposed method requires a minimal amount of interaction with the real-world as it pushes the exploration towards the xDTs. We validate our approach through real-world experiments, demonstrating its effectiveness in transferring and fine-tuning a NMPC with 9 parameters, in under 10 minutes. This eliminates the need for hours-long manual tuning and lengthy machine learning training and data collection phases. Our results show that the online adapted NMPC directly compensates for the Sim2Real gap and avoids overtuning in simulation. Importantly, a 75% improvement in tracking performance is achieved and the Sim2Real gap over the target performance is reduced from a factor of 876 to 1.033.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
