RDT-1B is a state-of-the-art imitation learning diffusion transformer with 1 billion parameters (currently the largest). It has been pre-trained on over 1 million multimodal robot scenarios. Given language instructions and RGB images from up to three views, RDT can predict the next 64 robot actions. RDT is compatible with nearly all modern mobile manipulators, including single to dual-arm systems, joint to end-effector configurations, position to velocity control, and even wheeled motions. The model has been fine-tuned on over 6,000 self-collected bimanual scenarios and deployed on the ALOHA dual-arm robot, achieving leading performance in dexterity, zero-shot generalization, and few-shot learning.