# open-gigaai/giga-brain-0

GigaBrain-0: A World Model-Powered Vision-Language-Action Model

Repository: https://github.com/open-gigaai/giga-brain-0
Canonical: https://ross.abutalabs.com/products/giga-brain-0
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2026-08-25T19:34:37+00:00

## Health v2 (maintenance only)
Score: 62/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 35, longevity 24
- inputs: {"age_days": 341, "days_push": 8, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2611, forks 205 (observed 2026-08-28T04:07:04.367154+00:00)

## What it is
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
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, llm-inference, simulation, robotics
- domain: robotics, machine-learning, deep-learning, artificial-intelligence, autonomous-vehicles
- platform: python
- tags: vision-language-action, vla, world-model, embodied-ai, foundation-model, robot-manipulation, reinforcement-learning, model-weights, linux, gpu, docker

## Member repositories
- open-gigaai/giga-brain-0 (main) score 62

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:04.367154+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:20:52.205772+00:00, confidence not recorded.
  - readme: https://github.com/open-gigaai/giga-brain-0 (fetched 2026-08-28T04:07:04.367154+00:00, sha 104bd79aa7ee)
- Data as of 2026-08-30T08:39:29.467469+00:00.