用控制理论在线优化学习，提升数据噪声下的适应能力
Online Control-Informed Learning
- 基于扩展卡尔曼滤波在线调整系统参数
- 三种模式实验验证有效，抗噪能力强
- 适合机器人实时学习与控制场景
本文提出一种在线控制感知学习（OCIL）框架，利用控制领域的最优控制与状态估计技术，以在线方式解决广泛的学习任务。该方法通过将任意机器人视为可调最优控制系统，基于扩展卡尔曼滤波（EKF）设计在线参数估计算法，实现增量式参数调节，使系统能完成指定学习或控制任务。该方法有效处理了机器学习中的噪声数据、在线学习和数据效率问题，并通过理论分析证明了其收敛性。实验验证了三种学习模式：在线模仿学习、在线系统辨识和即时策略调优，均表现出良好效果。
原文摘要 · Abstract (English)
This paper proposes an Online Control-Informed Learning (OCIL) framework, which employs the well-established optimal control and state estimation techniques in the field of control to solve a broad class of learning tasks in an online fashion. This novel integration effectively handles practical issues in machine learning such as noisy measurement data, online learning, and data efficiency. By considering any robot as a tunable optimal control system, we propose an online parameter estimator based on extended Kalman filter (EKF) to incrementally tune the system in an online fashion, enabling it to complete designated learning or control tasks. The proposed method also improves the robustness in learning by effectively managing noise in the data. Theoretical analysis is provided to demonstrate the convergence of OCIL. Three learning modes of OCIL, i.e. Online Imitation Learning, Online System Identification, and Policy Tuning On-the-fly, are investigated via experiments, which validate their effectiveness.
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