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The Berkeley Intelligent Control (ICON) lab develops algorithms for autonomous systems to interact with other agents safely and intelligently. Our goal is to enable autonomous systems to become integrated into the fabric of human life and act in the favor of society. To this end, we draw from control theory, game theory, robotics, and machine learning.
news
| Jan 28, 2025 | Our paper titled “CurricuLLM: Automatic Task Curricula Design for Learning Complex Robot Skills using Large Language Models” got accepted to the IEEE International Conference on Robotics and Automation (ICRA) 2025! |
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| Jan 18, 2025 | Our paper titled “To What Extent do Open-loop and Feedback Nash Equilibria Diverge in General-Sum Linear Quadratic Dynamic Games?” got accepted to the IEEE Control Systems Letters (L-CSS) and also for presentation at the 2025 American Control Conference(ACC)! |
| Dec 19, 2024 | Our paper titled “Leveraging Large Language Models for Effective and Explainable Multi-Agent Credit Assignment” got accepted at the International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2025! |
| Aug 24, 2024 | Our paper titled “Risk-Sensitive Orbital Debris Collision Avoidance using Distributionally Robust Chance Constraints” got accepted at the American Institute of Aeronautics and Astronautics (AIAA) SciTech Forum 2025! |
| Jul 24, 2024 | Our paper titled “Learning to Provably Satisfy High Relative Degree Constraints for Black-Box Systems” got accepted at the 2024 Conference on Decision and Control (CDC)! |