We are interested in building intelligent and useful robotic systems that can be deployed reliably in challenging environments. Our research broadly focuses on the intersection of machine learning and robotics, with an emphasis on large-scale robot learning (foundation models of & for robotics), reinforcement learning, and multi-agent and human-robot interaction.
We take a full-stack approach to robotics by focusing on all aspects of the problem, ranging from algorithmic innovation to system design, and value both the analytical and empirical nature of science. We draw inspiration from cognitive science and psychology to build physical AI systems at the interface of perception, learning, and control.
Robot Foundation Models
Foundations of scaling up robot learning: architectures (vision-language-action models, world models, etc.), systematic and scalable evaluation, understanding data quality, learning from diverse data sources, cross-embodiment learning.
Relevant Publications
Interaction and Autonomous Improvement
Algorithmic frameworks for modeling multi-agent and human robot interaction, learning from feedback, continual learning, autonomous improvement at scale.
Relevant Publications
Open-World Systems
Robust and reliable systems for long-term deployment of robots in the real world.
Relevant Publications