Driving · 2026-10-22
A Decade of Learning-Based Control: From Theoretic Foundations to Practical Deployment
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
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TL;DR
Tutorial reviewing learning-based control advances over the past decade, focusing on combining machine learning with control-theoretic structure to design autonomous systems with stability and safety guarantees. It covers foundations for stability/safety, dynamics learning for control under uncertainty, and direct data-driven policy synthesis/optimization (RL, imitation learning, Bayesian optimization). The authors also share implementations and illustrative examples and provide additional resources on a project website.
Why it matters
Learning-based control is presented as a way to extend autonomous systems while preserving closed-loop properties required for reliable deployment. The paper organizes method families by what is learned, uncertainty/estimation-error handling, compatible controller synthesis methods, and resulting closed-loop guarantees, connecting stability/safety theory (Lyapunov, ISS, stochastic notions, invariance, safety filters like PSF and CBF/HJ reachability) with learning paradigms (structured model learning, data-driven predictive control, and direct policy learning/tuning).
Method
- Reviews control-theoretic foundations for stability (nominal, robust ISS, stochastic) and for safety via invariance and safety filters (e.g., CBF, HJ reachability, PSF).
- Surveys learning-based control methods that learn dynamics models and improve control under uncertainty, including structured linear regression, neural networks, Bayesian linear regression, and Gaussian processes.
- Discusses direct data-driven control and controller optimization, including model-free/model-based RL, imitation learning/approximate MPC, and Bayesian optimization controller tuning with safety constraints.
Abstract (from arXiv)
Motivated by ten years of Learning-Based Control Invited Sessions at the IEEE Conference on Decision and Control (CDC), this tutorial paper reviews key advances at the intersection of machine learning and control. We focus on methods that combine the flexibility of machine learning techniques with the structure and theoretical foundations of control to design autonomous systems that are capable, reliable, and safe. We first introduce core notions for establishing stability and safety guarantees, then review approaches that learn dynamics models to improve control under uncertainty, and finally discuss data-driven methods that directly synthesize or learn policies, including reinforcement learning, imitation learning, and Bayesian optimization. We further discuss representative applications and open challenges. The goal is to provide an accessible entry point to the field and to support continued progress toward reliable learning-based control in real-world systems. To support learning and practical exploration, we share implementations and illustrative examples along with our discussion, together with additional resources on our website: https://learning-based-control.github.io/tutorial/.
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Publication record
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First public release: 2026-10-21 · Indexed by Pub-AI: 2026-10-22 UTC
A Decade of Learning-Based Control: From Theoretic Foundations to Practical Deployment
Preprint posted: 2026-10-21 · Original record ↗
- arxiv
- 2610.29187
Explicit source versions (1)
- v1 · 2026-10-21
- Preprint available
Source date: 2026-10-21 · First detected: 2026-10-25 UTC · Historical baseline
Source evidence (1)
- Official evidence ↗ · atom:published (official metadata)
Abstract · arxiv
Motivated by ten years of Learning-Based Control Invited Sessions at the IEEE Conference on Decision and Control (CDC), this tutorial paper reviews key advances at the intersection of machine learning and control. We focus on methods that combine the flexibility of machine learning techniques with the structure and theoretical foundations of control to design autonomous systems that are capable, reliable, and safe. We first introduce core notions for establishing stability and safety guarantees, then review approaches that learn dynamics models to improve control under uncertainty, and finally discuss data-driven methods that directly synthesize or learn policies, including reinforcement learning, imitation learning, and Bayesian optimization. We further discuss representative applications and open challenges. The goal is to provide an accessible entry point to the field and to support continued progress toward reliable learning-based control in real-world systems. To support learning and practical exploration, we share implementations and illustrative examples along with our discussion, together with additional resources on our website: https://learning-based-control.github.io/tutorial/.