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 20, 2024: | Our paper titled "Batch Active Learning of Reward Functions from Human Preferences" got accepted to the ACM Transactions on Human-Robot Interaction (THRI). |
| Jan 30, 2024: | Our paper titled "A Generalized Acquisition Function for Preference-based Reward Learning" got accepted to the 2024 International Conference on Robotics and Automation (ICRA). |
| Dec 5, 2023: | Our paper titled "Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback" got accepted to the Transactions on Machine Learning Research (TMLR). |
| Sep 25, 2023: | Our paper titled "Active Preference-Based Gaussian Process Regression for Reward Learning and Optimization" got accepted to the International Journal of Robotics Research (IJRR). |
| Sep 21, 2023: | Our paper titled "RoboCLIP: One Demonstration is Enough to Learn Robot Policies" got accepted to the 37th Conference on Neural Information Processing Systems (NeurIPS). |
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