alibaba-damo-academy/RynnVLA-002

Health v2 · maintenance only

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Adoption not part of the score

1119 stars · 66 forks observed · 2026-08-28

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

RynnVLA-002 is a unified autoregressive Vision-Language-Action and world model that generates robot actions from text and image observations and predicts future frames from actions. It includes model checkpoints, training code, and evaluation code for the LIBERO simulation benchmark and real-world LeRobot experiments.

Use cases

• train a vision-language-action model for robot manipulation
• evaluate robot policies on the LIBERO benchmark
• generate next-frame predictions with a world model
• run real-world robot experiments with LeRobot
• unify action generation and image generation in one model

When to choose

• you need a state-of-the-art VLA model for robotic manipulation research
• you want both action generation and world-model video prediction in a single framework
• you need LIBERO benchmark evaluation code and pretrained checkpoints

When to avoid

• you need a production robot control system rather than a research codebase
• you lack GPU resources for large autoregressive model training
• you need a permissively licensed dependency - the license field is unclear despite an Apache 2.0 badge

Facets

library · maturity active

machine-learning deep-learning computer-vision robotics llm-training robotics artificial-intelligence deep-learning computer-vision python vision-language-action world-model vla robot-learning autoregressive-model libero-benchmark lerobot linux gpu

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