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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.
Oct 20, 2020: Check Stanford HAI's blog post about our assistive feeding project here.
Oct 14, 2020: Our paper titled "Learning Latent Representations to Influence Multi-Agent Interaction" got accepted at the 4th Conference on Robot Learning (CoRL)!
Oct 8, 2020: Dorsa is giving a talk on "Interaction-Aware Planning: A Human-Centered Approach toward Autonomous Driving " at the IPAM Workshop on Individual Vehicle Autonomy: Perception and Control on October 8.
Oct 6, 2020: Our paper titled "BLEU Neighbors: A Reference-less Approach to Automatic Evaluation" got accepted at the 1st Workshop on Evaluation and Comparison for NLP systems (Eval4NLP)!
Sep 29, 2020: Our paper titled "Learning Adaptive Language Interfaces through Decomposition" got accepted at the First Workshop on Interactive and Executable Semantic Parsing @ EMNLP 2020!
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