Foundation Models

RDT-1B

RDT-1B (Robotics Diffusion Transformer) is a roughly 1.2-billion-parameter diffusion-based foundation model for bimanual manipulation, released by Tsinghua University researchers in 2024. It is pretrained on large multi-robot datasets using a unified action space that accommodates heterogeneous embodiments, then fine-tuned on a self-collected dataset of over 6,000 bimanual episodes on an ALOHA platform, demonstrating language-conditioned dexterous tasks and few-shot adaptation.

Why it matters for physical AI

RDT-1B showed that diffusion policies scale to foundation-model size and that unified action representations enable cross-embodiment pre-training, both influential design choices for bimanual robot learning.