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.
|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!|
|Jul 10, 2019:||Erdem will be giving an oral presentation at ACC 2019 on "Efficient and Safe Exploration in Deterministic Markov Decision Processes with Unknown Transition Models"!|
|Jun 25, 2019:||Stanford News compiled a story about our work on "Learning Reward Functions by Integrating Human Demonstrations and Preferences". Check it out here!|
|Jun 25, 2019:||We posted our new blogpost on "Influencing Leading and Following in Human-Robot Teams".|