Q-Transformer

Offline RL at scale: Trains multi-task policies from large offline datasets combining human demonstrations and autonomously collected robot data.

Transformer policy: Uses a transformer backbone with Q-learning, bridging the scaling properties of transformers with the data-efficiency of Q-learning.

Strong robotics performance: Achieves strong performance on a large diverse real-world robotic manipulation task suite - not just simulation.

Scaling signal for robotics: A significant early demonstration that transformer + Q-learning scales on real-world robot data, pointing toward foundation models for robotic control.