Q-Transformer
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Google's Q-Transformer is a scalable RL method for training multi-task robotic policies from large offline datasets.
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.
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