InternVLA-A1: Unifying Understanding, Generation and Action for Robotic Manipulation
InternVLA-A1 integrates understanding, generation, and action experts via a Mixture-of-Transformers (MoT) framework, 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 heterogeneous data sources over real-robot data, synthetic simulation data, and human videos
- InternVLA-A1-3B-RoboTwin: finetuned on RoboTwin 2.0 benchmark
- InternVLA-A1-3B-Pretrain-InternData-A1: pretrained on InternData-A1 only
- InternVLA-A1-2B-Pretrain-InternData-A1: pretrained on InternData-A1 only
Evaluation on RoboTwin 2.0 Simulation Benchmark
Setting: All models are jointly fine-tuned across 50 tasks (50 clean + 500 randomized demos each).
Performance Summary: InternVLA-A1-3B achieves the highest success rates across both Easy and Hard settings on the RoboTwin 2.0 Benchmark (averaged over 50 tasks).
| Metric | pi0 | pi0.5 | InternVLA-A1-3B |
|---|---|---|---|
| Avg. Success (Easy) | 79.98% | 86.76% | 89.40% |
| Avg. Success (Hard) | 79.50% | 86.96% | 89.64% |
🔑 Key Features
- 🔮 The Core: Synergizes MLLM's semantic understanding with world-model-style dynamic prediction, enabling it to "imagine" the future and guide adaptive actions.
- 🚀 The Fuel: Enables joint training on heterogeneous data sources over real-world robot data, synthetic simulation data, and egocentric human videos.
- ⚡ The Output: Tackles highly dynamic scenarios with effortless mastery.
Usage
Please refer to our official repo InternVLA-A1.
Demonstrations
InternVLA-A1 exhibits consistent robustness across static manipulation, dynamic manipulation, and simulation benchmarks, especially demonstrating remarkable superiority in dynamic scenarios.
⚡ Dynamic Manipulation Tasks
🤖 Static Manipulation Tasks
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{internvla_a1,
title={InternVLA-A1: Unifying Understanding, Generation and Action for Robotic Manipulation},
author={Cai, Junhao and Cai, Zetao and Cao, Jiafei and Chen, Yilun and He, Zeyu and Jiang, Lei and Li, Hang and Li, Hengjie and Li, Yang and Liu, Yufei and others},
journal={arXiv preprint arXiv:2601.02456},
year={2026}
}