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 NewsCheck out our YouTube channel for latest talks and supplementary videos for our publications.
|May 5, 2021:||Our paper titled "Incentivizing Efficient Equilibria in Traffic Networks with Mixed Autonomy" got accepted to the IEEE Transactions on Control of Network Systems (TCNS)!|
|May 4, 2021:||Our paper titled "Learning Visually Guided Latent Actions for Assistive Teleoperation" got accepted to the 3rd Annual Learning for Dynamics & Control Conference (L4DC)!|
|Apr 28, 2021:||We posted our new blogpost "On the Critical Role of Conventions in Adaptive Human-AI Collaboration".|
|Mar 10, 2021:||Our paper titled "Learning from Imperfect Demonstrations from Agents with Varying Dynamics" got accepted to the IEEE Robotics and Automation Letters (RA-L)!|
|Feb 28, 2020:||
Our 2 papers got accepted at the International Conference on Robotics and Automation 2021 (ICRA):
- "Learning Human Objectives from Sequences of Physical Corrections" by Li et al.
- "ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes" by Li et al.