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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
| 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! |
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| 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! |
| Aug 15, 2022 | We will organize a workshop on Strategic Multi-Agent Interactions: Game Theory for Robot Learning and Decision Making” at the Conference on Robot Learning (CoRL) 2022! |
| Apr 22, 2022 | We will organize a workshop on Behavior-driven Autonomous Driving in Unstructured Environments at the International Conference on Intelligent Robots and Systems (IROS) 2022! |
| Feb 28, 2022 | Our paper titled “ Congestion-aware Bi-modal Delivery Systems Utilizing Drones” got accepted at the European Control Conference (ECC) 2022! |