Embodied VLAs: RT-2, OpenVLA, π0, GR00T

The first time a model read a recipe off a website and executed it in a kitchen robot was RT-2 (Google DeepMind, July 2023). RT-2 discretized actions as text tokens, co-fine-tuned a VLM on web data plus robot-action data, and proved that web-scale vision-language knowledge transfers to robotic control. OpenVLA (June 2024) shipped the open 7B reference. Physical Intelligence's π0 series (2024-2025) added flow-matching action experts. NVIDIA's GR00T N1 (March 2025) delivered dual-system (System 1 / System 2) control for humanoid robots at scale. The VLA primitive — vision-language-action, a single model that sees, reads, and acts — is the bridge between this phase's understanding models and the autonomous systems in Phase 15.

Learn 3 hr Python (stdlib, action tokenizer + VLA inference skeleton)

Prerequisites: Phase 12 · 05 (LLaVA), Phase 15 (Autonomous Systems, referenced)

#Learning Objectives

Describe action tokenization: discrete bin encoding (RT-2), FAST efficient action tokens, continuous flow-matching actions (π0).
Explain why co-fine-tuning on web + robot data preserves general-knowledge transfer to novel tasks.
Compare OpenVLA (open 7B Llama+VLM), π0 (flow-matching), and GR00T N1 (dual-system) on the same robot task.
Name the Open X-Embodiment dataset and its role as the RT-X training corpus.

#Learning Objectives

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