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.| Feb 21, 2026: | Our paper titled "ORIC: Benchmarking Object Recognition under Contextual Incongruity in Large Vision-Language Models" got accepted to IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2026. |
| Feb 20, 2026: | Erdem has been awarded the 2026 ONR Young Investigator Program (YIP) Award! |
| Jan 31, 2026: |
Our 4 papers got accepted to the 2026 International Conference on Robotics and Automation (ICRA): - AutoFocus-IL: VLM-based Saliency Maps for Data-Efficient Visual Imitation Learning without Extra Human Annotations - HAND Me the Data: Fast Robot Adaptation via Hand Path Retrieval - IMPACT: Intelligent Motion Planning with Acceptable Contact Trajectories via Vision-Language Models - PEEK: Guiding and Minimal Image Representations for Zero-Shot Generalization of Robot Manipulation Policies |
| Jan 26, 2026: |
Our 2 papers got accepted to the 2026 International Conference on Learning Representations (ICLR): - Causally Robust Reward Learning from Reason-Augmented Preference Feedback - When a Robot is More Capable than a Human: Learning from Constrained Demonstrators |
| Jan 11, 2026: | Our paper titled "Value Explicit Pretraining for Learning Transferable Representations" got accepted to the IEEE Robotics and Automation Letters (RA-L). |
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