RT-2
Google DeepMind's end-to-end vision-language-action model that learns from both web and robotics data to control robots.
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VLA architecture: Treats robot actions as another language the model generates - actions are tokenized and output in the same stream as text tokens.
Web-scale knowledge transfer: Leverages internet-scale VLM pretraining so the robot can reason about novel objects and symbols it never saw in robotics data (e.g., "pick up the extinct animal").
Emergent semantic reasoning: Shows emergent capabilities like chain-of-thought robotic reasoning and multi-stage task planning absent in prior RT-1.
Robot foundation models: Established the VLA paradigm that dominated 2024 robotics research (OpenVLA, RT-X, π0) and moved robotics firmly into the foundation-model era.