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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
| 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! |
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| Mar 28, 2024 | Our paper titled “Adaptive Learning from Demonstration in Heterogeneous Agents: Concurrent Minimization and Maximization of Surprise in Sparse Reward Environments” got accepted at the Learning for Dynamics & Control (L4DC) 2024! |
| Jan 29, 2024 | Our paper titled “ Integrating Predictive Motion Uncertainties with Distributionally Robust Risk-Aware Control for Safe Robot Navigation in Crowds” got accepted at the International Conference on Robotics and Automation (ICRA) 2024! |
| Mar 10, 2023 | Our paper titled “RAPID: Autonomous Multi-agent Racing using Constrained Potential Dynamic Games” got accepted at the European Control Conference (ECC) 2023! |
| Jan 16, 2023 | Our paper titled “Learning to Influence Vehicles’ Routing in Mixed-Autonomy Networks by Dynamically Controlling the Headway of Autonomous Cars” got accepted at the International Conference on Robotics and Automation (ICRA) 2023! |