We research, build, and scale open-source reinforcement learning frameworks to make autonomous robots adaptable to real-world environments.
Developing foundational models for robotic manipulation, locomotion, and simulation.
Training robust neural networks in ultra-fast physics simulations and safely deploying them onto hardware.
Leveraging human demonstrations and vision-language models to teach robots complex everyday tasks.
Maintaining reproducible datasets and public environments to measure progress across the scientific community.