open-gigaai/giga-brain-0

Health v2 · maintenance only

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Adoption not part of the score

2611 stars · 205 forks observed · 2026-08-28

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

GigaBrain-0/0.7 is an open-source vision-language-action (VLA) model family for generalist embodied agents, powered by world models and a three-system architecture. It provides model architecture, pre-training and post-training code, HuggingFace model weights, and sample data for robot manipulation research.

Use cases

• train a vision-language-action model for robot manipulation
• run a VLA model on a real robot arm
• use world model-based reinforcement learning for embodied agents
• evaluate VLA models on RoboTwin or RoboChallenge benchmarks
• fine-tune a generalist robot policy on custom manipulation datasets
• research long-horizon task completion with embodied foundation models

When to choose

• you need an open VLA model with released weights for robot manipulation research
• you want to experiment with world model-powered embodied agents
• you're participating in the GigaBrain Challenge or similar embodied AI benchmarks

When to avoid

• you need a production robot control stack rather than a research model
• you lack GPU resources for large vision-language-action model training or inference
• you need non-manipulation robotics like locomotion or navigation

Facets

library · maturity active

machine-learning deep-learning llm-inference simulation robotics robotics machine-learning deep-learning artificial-intelligence autonomous-vehicles python vision-language-action vla world-model embodied-ai foundation-model robot-manipulation reinforcement-learning model-weights linux gpu docker

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