发表机构
TRATON; KTH Royal Institute of Technology（特拉顿集团; 瑞典皇家理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MetaKoopman作为贝叶斯元学习框架，通过学习Koopman算子先验实现分布偏移下非线性动力学的精准建模与预测，在卡车挂车系统的野外及模拟任务中表现优于现有方法，可用于鲁棒运动规划。
AI 中文摘要
在分布偏移下对非线性动力学进行建模与预测，是实现真实系统鲁棒决策的关键。本研究提出MetaKoopman，这是一种通过线性隐表征对非线性动力学进行建模的贝叶斯元学习框架。MetaKoopman学习Koopman算子的矩阵正态-逆威沙特（MNIW）先验，可基于近期轨迹片段实现闭式贝叶斯更新，还能提供未来状态轨迹的闭式后验预测分布，捕捉已学动力学中的认知与偶然不确定性。我们在全尺寸卡车挂车系统的多种不利冬季场景（含雪、冰及混合摩擦工况），以及具有多样分布偏移的模拟控制任务中对MetaKoopman进行评估。MetaKoopman在多步预测精度、不确定性校准及分布偏移鲁棒性方面始终优于现有方法，野外实验进一步证明其在动态可行运动规划中的有效性，尤其适用于避障机动及牵引力极限工况。项目网站：this https URL
英文摘要
Modeling and forecasting nonlinear dynamics under distribution shifts is essential for robust decision-making in real-world systems. In this work, we propose MetaKoopman, a Bayesian meta-learning framework for modeling nonlinear dynamics through linear latent representations. MetaKoopman learns a Matrix Normal-Inverse Wishart (MNIW) prior over the Koopman operator, enabling closed-form Bayesian updates conditioned on recent trajectory segments. Moreover, it provides a closed-form posterior predictive distribution over future state trajectories, capturing both epistemic and aleatoric uncertainty in the learned dynamics. We evaluate MetaKoopman on a full-scale autonomous truck and trailer system across a wide range of adverse winter scenarios, including snow, ice, and mixed-friction conditions, as well as in simulated control tasks with diverse distribution shifts. MetaKoopman consistently outperforms prior approaches in multi-step prediction accuracy, uncertainty calibration, and robustness to distributional shifts. Field experiments further demonstrate its effectiveness in dynamically feasible motion planning, particularly during evasive maneuvers and operation at the limits of traction. Project website: https://mahmoud-selim.github.io/MetaKoopman/
Journal refAdvances in Neural Information Processing Systems 38 (2025), 29551-29584
