robotics-diffusion-transformer
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RDT2-VQ

image-text-to-text

Vision-Language-Action

text-generation-inference

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robotics-diffusion-transformer commited on Feb 5

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 It predicts a short-horizon **relative action chunk** (24 steps, 20 dims/step) from binocular wrist-camera RGB and a natural-language instruction.
 Actions are discretized with a lightweight **Residual VQ (RVQ)** tokenizer, enabling robust zero-shot transfer across **unseen embodiments** for simple, open-vocabulary skills (e.g., pick, place, shake, wipe).
-[**Home**](https://rdt-robotics.github.io/rdt2/) - [**Github**](https://github.com/thu-ml/RDT2/tree/main?tab=readme-ov-file) - [**Discord**](https://discord.gg/vsZS3zmf9A)
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 It predicts a short-horizon **relative action chunk** (24 steps, 20 dims/step) from binocular wrist-camera RGB and a natural-language instruction.
 Actions are discretized with a lightweight **Residual VQ (RVQ)** tokenizer, enabling robust zero-shot transfer across **unseen embodiments** for simple, open-vocabulary skills (e.g., pick, place, shake, wipe).
+[**Home**](https://rdt-robotics.github.io/rdt2/) - [**Github**](https://github.com/thu-ml/RDT2/tree/main?tab=readme-ov-file) - [**Discord**](https://discord.gg/vsZS3zmf9A) - [**Paper**](https://arxiv.org/abs/2602.03310)
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