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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.
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"
Sep 12, 2022: Our 5 papers got accepted to the 6th Conference on Robot Learning (CoRL) 2022:
- "Learning Visuo-Haptic Skewering Strategies for Robot-Assisted Feeding"
- "Few-Shot Preference Learning for Human-in-the-Loop RL"
- "Eliciting Compatible Demonstrations for Multi-Human Imitation Learning"
- "Learning Bimanual Scooping Policies for Food Acquisition"
- "PLATO: Predicting Latent Affordances Through Object-Centric Play"
May 15, 2022: Our paper titled "Imitation Learning by Estimating Expertise of Demonstrators" got accepted to the 39th International Conference on Machine Learning (ICML)!
Jan 31, 2022: Our 4 papers got accepted to the International Conference on Robotics and Automation (ICRA) 2022:
- "Balancing Efficiency and Comfort in Robot-Assisted Bite Transfer"
- "Learning from Imperfect Demonstrations via Adversarial Confidence Transfer"
- "Weakly Supervised Correspondence Learning"
- "Leveraging Smooth Attention Prior for Multi-Agent Trajectory Prediction"
Jan 6, 2022: Our 2 papers got accepted to the 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI):
- "APReL: A Library for Active Preference-based Reward Learning Algorithms" by Biyik et al.
- "Conditional Imitation Learning for Multi-Agent Games" by Shih et al.
See All

Recent Talk

Dorsa's keynote talk at the 4th Conference on Robot Learning (CoRL) on "Walking the Boundary of Learning and Interaction"