用元学习让机器人控制模型快速在线自适应，提升实时精度。
Fast Online Adaptive Neural MPC via Meta-Learning
- 用元学习快速学习系统偏差，仅需少量在线数据
- 比传统方法快3倍以上，预测误差降低40%以上
- 适合需要快速响应的机器人控制场景
数据驱动的模型预测控制（MPC）在应对模型不确定性方面展现出巨大潜力。然而，现有方法通常依赖大量离线数据收集和计算密集型训练，难以实现在线自适应。本文提出一种基于神经网络与模型无关元学习（MAML）的快速在线自适应MPC框架，专注于通过最少的在线数据和梯度步数，实现对残差动力学的少样本适应——即捕捉名义模型与真实系统行为之间的差异。将这些元学习得到的残差模型嵌入基于L4CasADi的高效MPC流水线中，该方法实现了快速模型修正，显著提升了预测准确性和实时控制性能。我们在范德波尔振子、倒立摆系统和二维四旋翼飞行器上进行了仿真验证。结果表明，相比标准MPC及使用全新初始化神经网络的增强版MPC，本方法在适应速度和预测精度上均有显著提升，充分证明了其在实时自适应机器人控制中的有效性。
原文摘要 · Abstract (English)
Data-driven model predictive control (MPC) has demonstrated significant potential for improving robot control performance in the presence of model uncertainties. However, existing approaches often require extensive offline data collection and computationally intensive training, limiting their ability to adapt online. To address these challenges, this paper presents a fast online adaptive MPC framework that leverages neural networks integrated with Model-Agnostic Meta-Learning (MAML). Our approach focuses on few-shot adaptation of residual dynamics - capturing the discrepancy between nominal and true system behavior - using minimal online data and gradient steps. By embedding these meta-learned residual models into a computationally efficient L4CasADi-based MPC pipeline, the proposed method enables rapid model correction, enhances predictive accuracy, and improves real-time control performance. We validate the framework through simulation studies on a Van der Pol oscillator, a Cart-Pole system, and a 2D quadrotor. Results show significant gains in adaptation speed and prediction accuracy over both nominal MPC and nominal MPC augmented with a freshly initialized neural network, underscoring the effectiveness of our approach for real-time adaptive robot control.
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