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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
| Feb 15, 2023: |
Our 5 papers got accepted to the International Conference on Robotics and Automation (ICRA) 2024: - "Physically Grounded Vision-Language Models for Robotic Manipulation" - "Toward Grounded Commonsense Reasoning" - "Distilling and Retrieving Generalizable Knowledge for Robot Manipulation via Language Corrections" - "How to Prompt Your Robot: A Prompt Book for Manipulation Skills with Code as Policies" - "Open X-Embodiment: Robotic Learning Datasets and RT-X Models" |
| Jan 30, 2024: | Our paper titled "Contrastive Preference Learning: Learning from Human Feedback without RL" got accepted to the International Conference on Learning Representations (ICLR) 2024! |
| Sep 21, 2023: |
Our 4 papers got accepted to the Conference on Neural Information Processing Systems (NeurIPS) 2023: - Parallel Sampling of Diffusion Models (Spotlight) - Diverse Conventions for Human-AI Collaboration - Data Quality in Imitation Learning - Inverse Preference Learning: Preference-based RL without a Reward Function |
| Aug 30, 2023: |
Our 7 papers got accepted to the Conference on Robot Learning (CoRL) 2023: - Stabilize to Act: Learning to Coordinate for Bimanual Manipulation (Oral) - HYDRA: Hybrid Robot Actions for Imitation Learning - Learning Sequential Acquisition Policies for Robot-Assisted Feeding - Gesture-Informed Robot Assistance via Foundation Model - KITE: Keypoint-Conditioned Policies for Semantic Manipulation - Large Language Models as General Pattern Machines - Polybot: Training One Policy Across Robots While Embracing Variability |
| 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" |
| See All |