Precise control in modern robotic applications is always an open issue due to unknown time-varying disturbances. Existing meta-learning-based approaches require a shared representation of environmental structures, which lack flexibility for realistic non-structural disturbances. Besides, representation error and the distribution shifts can lead to heavy degradation in prediction accuracy. This work presents a generalizable disturbance estimation framework that builds on meta-learning and feedback-calibrated online adaptation. By extracting features from a finite time window of past observations, a unified representation that effectively captures general non-structural disturbances can be learned without predefined structural assumptions. The online adaptation process is subsequently calibrated by a state-feedback mechanism to attenuate the learning residual originating from the representation and generalizability limitations. Theoretical analysis shows that simultaneous convergence of both the online learning error and the disturbance estimation error can be achieved. Through the unified meta-representation, our framework effectively estimates multiple rapidly changing disturbances, as demonstrated by quadrotor flight experiments. See the project page for video, supplementary material and code: https://nonstructural-metalearn.github.io.

翻译：现代机器人应用中的精确控制始终是一个开放性问题，这源于未知的时变扰动。现有的基于元学习的方法需要环境结构的共享表示，这缺乏应对现实非结构性扰动的灵活性。此外，表示误差与分布偏移可能导致预测精度严重下降。本文提出了一种基于元学习与反馈校准在线适应的通用扰动估计框架。通过从过去观测的有限时间窗口中提取特征，可以学习到一个统一表示，该表示能有效捕捉一般的非结构性扰动，而无需预定义的结构性假设。在线适应过程随后通过状态反馈机制进行校准，以衰减源自表示与泛化能力限制的学习残差。理论分析表明，在线学习误差与扰动估计误差可同时收敛。通过统一的元表示，我们的框架能有效估计多种快速变化的扰动，这已在四旋翼飞行实验中得以验证。项目页面提供视频、补充材料与代码：https://nonstructural-metalearn.github.io。