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.| Mar 21, 2025: | We are organizing a workshop on human-in-the-loop robot learning at RSS 2025. Check it out here for more details. |
| Jan 27, 2025: |
Our 2 papers got accepted to the 2025 International Conference on Robotics and Automation (ICRA): - MILE: Model-based Intervention Learning - Multi-Agent Inverse Q-Learning from Demonstrations |
| Dec 19, 2024: | Our paper titled "Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback" has been selected as a finalist for TMLR's 2024 outstanding papers. |
| Sep 25, 2024: | Our paper titled "DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning" got accepted to the 38th Conference on Neural Information Processing Systems (NeurIPS). |
| Sep 20, 2024: | Our paper titled "Accurate and Data-Efficient Toxicity Prediction when Annotators Disagree" got accepted to the Conference on Empirical Methods in Natural Language Processing (EMNLP). |
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