オンライン制御情報学習
Online Control-Informed Learning
最適制御と状態推定を組み合わせ、EKFによるオンラインパラメータ推定でロボットの学習・制御タスクをノイズに強く効率的に実行する枠組みを提案。
著者: Zihao Liang, Tianyu Zhou, Zehui Lu, Shaoshuai Mou
分類: math.OC, cs.LG, cs.RO, cs.SY, eess.SY
原文アブストラクト
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.
