license: cc-by-nc-sa-4.0
base_model:
- Qwen/Qwen3-VL-2B-Instruct
tags:
- robotics
- vision-language-action-model
datasets:
- InternRobotics/InternData-A1
InternVLA-A1: Unifying Understanding, Generation and Action for Robotic Manipulation
InternVLA-A1 integrates understanding, generation, and action experts into a unified model, which synergizes MLLMs' semantic reasoning with world-model-style dynamics prediction to guide action execution.
Building upon InternVL3 and Qwen3-VL, we instantiate InternVLA-A1 at 2B and 3B parameter scales. Covering different model scales and pre-training data configurations, we release the InternVLA-A1 series:
- InternVLA-A1-3B: pretrained on the large-scale, high-fidelity simulation data InternData-A1, together with open-source robot data (e.g. Agibot-World)
- InternVLA-A1-3B-Pretrain-InternData-A1: pretrained on InternData-A1 only
- InternVLA-A1-2B-Pretrain-InternData-A1: pretrained on InternData-A1 only
🔑 Key Features
Regarding model architecture, InternVLA-A1 employs a Mixture-of-Transformers (MoT) design to unifies scene understanding, visual foresight, and action execution into a single framework. It synergizes MLLM's semantic understanding with world-model-style dynamic prediction, to "imagine" the future and guide adaptive actions.
Regarding training data, We pre-train InternVLA-A1 on hybrid synthetic-real datasets spanning InternData-A1 and open-source real-world data (e.g. Agibot-World). Our hybrid synthetic-real pre-training strategy combines the scene diversity of simulation with the physical fidelity of real-world data.
Usage
Please refer to our official repo InternVLA-A1.
Demonstrations
⚡ Dynamic Manipulation
InternVLA-A1 exhibits exceptional robustness in highly dynamic scenarios.
🤖 Daily tasks
InternVLA-A1 also demonstrates superior proficiency in dexterous and fine-grained manipulation.
License and Citation
All the code within this repo are under CC BY-NC-SA 4.0. Please consider citing our project if it helps your research.
@article{contributors2026internvla_a1,
title={InternVLA-A1: Unifying Understanding, Generation and Action for Robotic Manipulation},
author={InternVLA-A1 contributors},
journal={arXiv preprint arXiv:2601.02456},
year={2026}
}