# starVLA/starVLA

StarVLA: A Lego-like Codebase for Vision-Language-Action Model Developing

Repository: https://github.com/starVLA/starVLA
Canonical: https://ross.abutalabs.com/products/starvla
Homepage: https://starvla.github.io/
Language: Python
License: NOASSERTION
License Family: other
Topics: robotic-foundation-model, robotics, vision-language-action-model, world-action-model
Last push: 2026-08-09T17:00:59+00:00

## Health v2 (maintenance only)
Score: 69/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 96, release rhythm 60, longevity 23
- inputs: {"age_days": 328, "days_push": 24, "days_rel": 104, "gap_med": 125, "n_releases_24m": 2}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3530, forks 466 (observed 2026-08-28T04:08:08.867131+00:00)

## What it is
StarVLA is an open-source, Lego-like modular codebase for developing Vision-Language-Action (VLA) models for generalist robots. It unifies model components, training recipes, benchmark evaluation, and deployment interfaces so researchers can plug-and-play modules and iterate rapidly.

## Use cases
- train vision-language-action models for robot manipulation
- evaluate robot policies on LIBERO, SimplerEnv, RoboCasa, RoboTwin, and BEHAVIOR benchmarks
- swap model backbones like Qwen into a VLA training pipeline
- deploy trained policies to real robots via a unified WebSocket interface
- prototype new robot foundation model architectures with plug-and-play components

## When to choose
- you are researching or developing VLA / robot foundation models and want modular, reproducible training and evaluation
- you need consistent benchmarking across multiple robot simulation environments
- you want to bridge simulation-trained policies to real-robot deployment without rewriting serving logic

## When to avoid
- you need a production-ready, commercially licensed robot control stack (license is non-standard)
- you only need simple robot control without learning-based policies
- you require a stable, long-term-supported API rather than an actively evolving research codebase

## Facets
- artifact type: framework
- maturity: active
- function: machine-learning, deep-learning, robotics, agent-framework, llm-training
- domain: robotics, machine-learning, artificial-intelligence, simulation
- platform: python
- tags: vision-language-action, vla, embodied-ai, robot-learning, foundation-model, modular-framework, benchmark-evaluation, sim2real, gpu, linux, docker

## Member repositories
- starVLA/starVLA (main) score 69

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:08.867131+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 2026-08-29T18:35:06.932666+00:00, confidence not recorded.
  - readme: https://github.com/starVLA/starVLA (fetched 2026-08-28T04:08:08.867131+00:00, sha 81f33dcf393e)
  - homepage: https://starvla.github.io/ (fetched 2026-08-29T09:28:59.364739+00:00, sha ec669b904e35)
- Data as of 2026-08-30T08:39:29.467469+00:00.