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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.| Apr 24, 2023: |
Our 4 papers got accepted to the International Conference on Machine Learning (ICML) 2023: - "Language Instructed Reinforcement Learning for Human-AI Coordination" - "Generating Language Corrections for Teaching Physical Control Tasks" - "Distance Weighted Supervised Learning: Robust Learning From Offline Interaction Data" - "Long Horizon Temperature Scaling" |
| Apr 24, 2023: |
Our 2 papers got accepted to the Proceedings of Robotics: Science and Systems (RSS) 2023: - "Behavior Retrieval: Few-Shot Imitation Learning by Querying Unlabeled Datasets" - "Language-Driven Representation Learning for Robotics" |
| Jan 30, 2023: | Our paper titled "Reward Design with Language Models" got accepted to the International Conference on Learning Representations (ICLR)! |
| Jan 30, 2023: |
Our 3 papers got accepted to the International Conference on Robotics and Automation (ICRA) 2023: - "In-Mouth Robotic Bite Transfer with Visual and Haptic Sensing" - "Learning Tool Morphology for Contact-Rich Manipulation Tasks with Differentiable Simulation" - "Active Reward Learning from Online Preferences" |
| Sep 15, 2022: |
Our 2 papers got accepted to the 36th Conference on Neural Information Processing Systems (NeurIPS) 2022: - "Assistive Teaching of Motor Control Tasks to Humans" - "Training and Inference on Any-Order Autoregressive Models the Right Way" |
| See All |