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Stanford Intelligent and Interactive Autonomous Systems Group (ILIAD) develops algorithms for autonomous systems that safely and reliably interact with people. Using the tools from artificial intelligence, control theory, robotics, machine learning, and optimization, we develop practical algorithms and the theoretical foundations for interactive robots working with people in uncertain, and safety-critical environments.

Recent News

Sep 20, 2019: Check our R-AL submission about controlling assistive robots on arXiv.
Sep 11, 2019: Our papers titled "Asking Easy Questions: A User-Friendly Approach to Active Reward Learning", and "Learning from My Partner's Actions: Roles in Decentralized Robot Teams" got accepted at CoRL 2019!
Sep 10, 2019: Check our preprint about learning dynamic routing of autonomous cars to decrease traffic congestion on arXiv.
Jul 19, 2019: Our paper titled "The Green Choice: Learning and Influencing Human Decisions on Shared Roads" got accepted at CDC 2019!
Jul 18, 2019: Our papers titled "Active Learning of Reward Dynamics from Hierarchical Queries", and "Robots that Take Advantage of Human Trust" got accepted at IROS 2019!
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