thu-ml/RoboticsDiffusionTransformer

Health v2 · maintenance only

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1778 stars · 162 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

RDT-1B is a 1B-parameter diffusion foundation model for robot bimanual manipulation, pre-trained on 1M+ multi-robot episodes to predict robot actions from language instructions and RGB images. The repo provides the official PyTorch implementation with model code, pre-trained checkpoints, training/sampling scripts, and real-robot deployment examples.

Use cases

• fine-tune a robot manipulation foundation model on my own dataset
• deploy a learned policy on a dual-arm ALOHA robot
• evaluate imitation learning policies in ManiSkill simulation
• predict robot actions from camera images and language instructions
• train a diffusion transformer for robotics with DeepSpeed
• run zero-shot manipulation with a pre-trained robotics model

When to choose

• you need a pre-trained foundation model for bimanual or single-arm manipulation
• you want to fine-tune a large robotics policy on custom episodes
• you need compatibility with varied manipulators (joint/EEF, position/velocity control)
• you want a state-of-the-art imitation learning baseline for manipulation benchmarks

When to avoid

• you need lightweight models for low-VRAM hardware (though RDT-170M partially addresses this)
• your task is not robot manipulation (e.g., navigation or locomotion only)
• you lack GPU resources for training or inference of billion-parameter models
• you need a plug-and-play product rather than a research codebase

Facets

library · maturity active

machine-learning deep-learning llm-training simulation robotics machine-learning deep-learning artificial-intelligence python diffusion-transformer imitation-learning robot-manipulation foundation-model bimanual-manipulation pytorch deepspeed aloha-robot maniskill gpu linux

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