arXiv Machine Learning
Oct 22

A Decade of Learning-Based Control: From Theoretic Foundations to Practical Deployment

This tutorial paper reviews a decade of advances at the intersection of machine learning and control, focusing on methods that blend machine learning flexibility with control theory’s structure to create capable, reliable, and safe autonomous systems. It covers core concepts for stability and safety, learning dynamics models to handle uncertainty, and data‑driven policy synthesis techniques such as reinforcement learning, imitation learning, and Bayesian optimization. Representative applications and open challenges are discussed, and the authors provide implementations, examples, and additional resources on their website.

By Angela P. Schoellig, Matthias A. M\"uller, Sebastian Trimpe, Melanie N. Zeilinger, Mahathi Anand, Andrea Carron, Oliver Hausd\"orfer, Victor G. Lopez, Friedrich Solowjow, Haocheng Zhao, Siqi Zhou
arXiv AI
Jul 3

Lightweight Safe Reinforcement Learning for End-to-End UAV Navigation

arXiv:2607. 01794v1 Announce Type: cross Abstract: With the rapid development of autonomous aerial systems, Unmanned Aerial Vehicles (UAVs) are increasingly deployed in applications such as inspection, environmental monitoring, and rescue, creating growing demand for reliable autonomous navigation.

By Shenghui Zhang, YuXuan Gao, Songwei Zhao, Jifeng Hu, Zijing Zhang, Hechang Chen