How cognitive AI (vision + language) turns into motion: the same big picture as models like
OpenVLA
— pretrained on large robot datasets (e.g. Open X-Embodiment), then fine-tuned for your robot and task.
This lesson is educational pseudocode, not a copy of any repo — it shows what layers exist and how they sync when you extend or fine-tune a .
The Hugging Face–shaped blocks below use placeholder IDs and helper names; always open the model card + README on the Hub for exact class names, prompt strings, and trust_remote_code requirements.
Tip: follow the highlighted pipeline box on the left.
Disclaimer: OpenVLA (and follow-ons like OpenVLA-OFT) use specific checkpoints, tokenizers, and action spaces from the paper and Hugging Face repos.
Class names like AutoModelForVision2Seq, helper names (extract_action_tokens), and PEFT target modules vary by checkpoint — copy from the official inference script.
This page is a schematic so you know what to open in code when you fine-tune or swap robots. Always match action dimensions, control rate, and safety limits to your hardware stack.