Online Control-Informed Learning

2024-10-04

arXiv.org

AI Summary

This work addresses key challenges in deploying machine learning on real-world robotic systems—namely, susceptibility to sensor and actuator noise, poor online adaptability, and low data efficiency. To this end, we propose a unified framework integrating optimal control with online learning. Our core innovation is modeling the robot as a tunable optimal control system and designing an Extended Kalman Filter (EKF)-based online parameter estimator, for which we provide theoretical guarantees on convergence and regret bound. Building upon this foundation, we introduce three novel online learning paradigms: imitation learning, system identification, and real-time policy refinement. The framework achieves strong robustness and high data efficiency, significantly improving learning stability and control accuracy under noisy conditions. It thus establishes a verifiable theoretical foundation and practical methodology for real-time, adaptive intelligent control in physical robotic systems.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Online Learning & Bandits
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Integrates control theory to solve online learning tasks efficiently.
• Handles noisy data and improves data efficiency in machine learning.
• Enhances robustness in learning by managing data noise effectively.

Innovation

• Online Control-Informed Learning framework
• Extended Kalman Filter for parameter estimation
• Handles noisy data, online learning, data efficiency