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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
| 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)! |
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| Jul 01, 2024 | Our paper titled “Optimal Robotic Assembly Sequence Planning: A Sequential Decision-Making Approach” has been accepted to the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2024! |
| May 15, 2024 | Our paper titled “POLICEd RL: Learning Closed-Loop Robot Control Policies with Provable Satisfaction of Hard Constraints” got accepted at the Robotics: Science and Systems (RSS) 2024! |
| Apr 04, 2024 | One of our students, Kartik Nagpal, was awarded the National Science Foundation Graduate Research Fellowship (NSF GRFP)! |
| Mar 28, 2024 | Our paper titled “ Adaptive Teaching in Heterogeneous Agents: Balancing Surprise in Sparse Reward Scenarios” got accepted at the Learning for Dynamics & Control Conference (L4DC) 2024! |