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Stanford Intelligent and Interactive Autonomous Systems Group (ILIAD) develops algorithms for AI agents that safely and reliably interact with people. Our mission is to develop theoretical foundations for human-robot and human-AI interaction. Our group is focused on: 1) formalizing interaction and developing new learning and control algorithms for interactive systems inspired by tools and techniques from game theory, cognitive science, optimization, and representation learning, and 2) developing practical robotics algorithms that enable robots to safely and seamlessly coordinate, collaborate, compete, or influence humans.

Recent News

Check out our YouTube channel for latest talks and supplementary videos for our publications.
Nov 19, 2020: We are organizing Bay Area Robotics Symposium (BARS) 2020 together with Mark Mueller. It will be on November 20. Live stream and more details are on the event web site: https://bars2020.github.io/.
Nov 19, 2020: Dorsa gave a keynote talk at the 4th Conference on Robot Learning (CoRL) on "Walking the Boundary of Learning and Interaction". See her talk here.
Nov 19, 2020: Check Stanford HAI's blog post about our reserch on "Learning Latent Representations to Influence Multi-Agent Interaction" here.
Nov 18, 2020: Our paper titled "Learning Latent Representations to Influence Multi-Agent Interaction" has won the best paper award at CoRL 2020! The award committee noted "a compelling solution to a difficult problem demonstrated on multiple domains including a competitive physical robot environment".
Nov 17, 2020: We posted our new blogpost on "Learning to Influence Multi-Agent Interaction".
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Recent Talk

Dorsa's keynote talk at the 4th Conference on Robot Learning (CoRL) on "Walking the Boundary of Learning and Interaction"