Foundation Models

RT-1

RT-1 (Robotics Transformer 1) is a 35-million-parameter transformer policy from Google, introduced in 2022, trained on roughly 130,000 real-world demonstrations spanning more than 700 tasks, collected over 17 months with a fleet of 13 mobile manipulators. It encodes images with a FiLM-conditioned EfficientNet, compresses tokens with TokenLearner, and outputs discretized arm and base actions at 3 Hz, demonstrating that large, diverse robot datasets yield broad task generalization.

Why it matters for physical AI

RT-1 was the proof of concept that scaling diverse real-robot data with a high-capacity architecture improves generalization, setting the data-scaling agenda that led to RT-2 and cross-embodiment training.