v211: Proceedings of L4DC 2023

2026-06-10 · Source: Proceedings of Machine Learning Research · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, long

Summary

Volume 211 of "The 5th Annual Learning for Dynamics and Control Conference" compiles research from June 2023, showcasing advancements at the intersection of machine learning and control theory. The papers frequently explore Reinforcement Learning (RL) for achieving safe, efficient, and robust control across diverse dynamical systems, often integrating physics-informed models and various neural network architectures, including Neural ODEs. Key challenges addressed include ensuring safety guarantees through methods like Control Barrier Functions and reachability analysis, managing uncertainty in state estimation, and optimizing performance in real-time and multi-agent environments. Contributions span a wide array of applications, from autonomous robotics and multi-agent coordination to traffic control, microfinance, and tokamak control. The research highlights progress in adaptive control, optimal control, and robust learning techniques, emphasizing both theoretical foundations and practical implementations.

Key takeaway

This volume from the 5th Annual Learning for Dynamics and Control Conference presents cutting-edge research on integrating machine learning with control theory for complex dynamical systems. Key contributions span robust and safe reinforcement learning, physics-informed models, neural network-based controllers, and efficient optimization techniques. Professionals in robotics, autonomous systems, and industrial control will find practical advances in areas like multi-agent coordination, real-time optimization, and certified system performance.

Topics

• Reinforcement Learning
• Dynamical Systems Control
• Neural Network Applications
• Safety-Critical Systems
• Multi-Agent Control

Best for: AI Scientist, Robotics Engineer, Research Scientist

Related on AIssential

• v168 — The 4th Annual Learning for Dynamics and Control Conference (L4DC 2022), held at Stanford University on June 23-24, 2022, showcased extensive research at the intersection of machine learning and control systems.
• v242: Proceedings of L4DC 2024 — The Volume 242 proceedings from the 6th Annual Learning for Dynamics & Control Conference, held July 15-17, 2024, at the University of Oxford, showcase cutting-edge research at the intersection of mac…
• Training-Free Agentic AI: Probabilistic Control and Coordination in Multi-Agent LLM Systems — Probabilistic control with belief-guided delegation significantly boosts multi-agent LLM system efficiency and robustness without training.
• From Kinematics to Dynamics: Learning to Refine Hybrid Plans for Physically Feasible Execution — Robotic plans based on first-order dynamics are physically infeasible; a neuro-symbolic RL framework can refine them for second-order feasibility.
• Complex & Intelligent Systems, Volume 12, Issue 1, January 2026 — The volume showcases diverse AI applications, emphasizing advanced neural networks, optimization, and multimodal data fusion.
• Post-Hoc Robustness for Model-Based Reinforcement Learning — Post-hoc robustification enhances deep RL agent resilience at inference time without retraining, using model-predictive control.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Proceedings of Machine Learning Research.