USC Learning and Interactive Robot Autonomy Lab (LiraLab) develops algorithms for robot learning, safe and efficient human-robot interaction and multi-agent systems. Our mission is to equip robots, or more generally agents powered with artificial intelligence (AI), with the capabilities that will enable them to intelligently learn, adapt to, and influence the humans and other AI agents. We take a two-step approach to this problem. First, machine learning techniques that we develop enable robots to model the behaviors and goals of the other agents by leveraging different forms of information they leak or explicitly provide. Second, these robots interact with the others to achieve online adaptation by leveraging the learned behaviors and goals while making sure this adaptation is beneficial and sustainable.
Recent News
Check out our YouTube channel for latest talks and supplementary videos for our publications.| Sep 4, 2026: | Our paper titled "Subspace Inference Enables Efficient Active Reward Learning from Preferences" got accepted to the Transactions on Machine Learning Research (TMLR) 2026. |
| Jun 17, 2026: | Our paper titled "CLAM: Continuous Latent Action Models for Robot Learning from Unlabeled Demonstrations" got accepted to the International Conference on Intelligent Robots and Systems (IROS) 2026. |
| May 21, 2026: | Erdem Bıyık was interviewed by Spectrum News about data collection from humans for humanoid robots. Access the interview here. |
| Apr 30, 2026: | Our paper titled "Fix the Mind, Not the Move: Interpretable AI Assistance via Knowledge-Gap Localization" got accepted to the International Conference on Machine Learning (ICML) 2026. |
| Apr 26, 2026: | Our paper titled "Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons" got accepted to the Robotics: Science and Systems (RSS) 2026 conference. |
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