This paper proposes an Online Control-Informed Learning (OCIL) framework, which synthesizes the well-established control theories to solve a broad class of learning and control tasks in real time. 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 real time, enabling it to complete designated learning or control tasks. The proposed method also improves robustness in learning by effectively managing noise in the data. Theoretical analysis is provided to demonstrate the convergence and regret 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.

翻译：本文提出了一种在线控制感知学习（OCIL）框架，该框架综合了成熟的控制理论，以实时解决广泛的学习与控制任务。这种新颖的整合有效处理了机器学习中的实际问题，如噪声测量数据、在线学习和数据效率。通过将任何机器人视为可调优的最优控制系统，我们提出了一种基于扩展卡尔曼滤波器（EKF）的在线参数估计器，以实时增量式调整系统，使其能够完成指定的学习或控制任务。所提方法还通过有效管理数据中的噪声，提高了学习的鲁棒性。本文提供了理论分析以证明OCIL的收敛性和遗憾界。通过实验研究了OCIL的三种学习模式，即在线模仿学习、在线系统识别和即时策略调优，验证了其有效性。