Saved in:
| Main Authors: | , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.16651 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915628766461952 |
|---|---|
| author | Tian, Yang Yang, Yuyin Xie, Yiman Cai, Zetao Shi, Xu Gao, Ning Liu, Hangxu Jiang, Xuekun Qiu, Zherui Yuan, Feng Li, Yaping Wang, Ping Cai, Junhao Zeng, Jia Dong, Hao Pang, Jiangmiao |
| author_facet | Tian, Yang Yang, Yuyin Xie, Yiman Cai, Zetao Shi, Xu Gao, Ning Liu, Hangxu Jiang, Xuekun Qiu, Zherui Yuan, Feng Li, Yaping Wang, Ping Cai, Junhao Zeng, Jia Dong, Hao Pang, Jiangmiao |
| contents | Recent works explore how real and synthetic data contribute to Vision-Language-Action (VLA) models' generalization. While current VLA models have shown the strong effectiveness of large-scale real-robot pre-training, synthetic data has not previously demonstrated comparable capability at scale. This paper provides the first evidence that synthetic data alone can match the performance of the strongest $π$-dataset in pre-training a VLA model, revealing the substantial value of large-scale simulation. The resulting model also exhibits surprisingly zero-shot sim-to-real transfer on several challenging tasks. Our synthetic dataset, InternData-A1, contains over 630k trajectories and 7,433 hours across 4 embodiments, 18 skills, 70 tasks, and 227 scenes, covering rigid, articulated, deformable, and fluid-object manipulation. It is generated through a highly autonomous, fully decoupled, and compositional simulation pipeline that enables long-horizon skill composition, flexible task assembly, and heterogeneous embodiments with minimal manual tuning. Using the same architecture as $π_0$, we pre-train a model entirely on InternData-A1 and find that it matches the official $π_0$ across 49 simulation tasks, 5 real-world tasks, and 4 long-horizon dexterous tasks. We release the dataset and will open-source the generation pipeline to broaden access to large-scale robotic data and to lower the barrier to scalable data creation for embodied AI research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16651 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | InternData-A1: Pioneering High-Fidelity Synthetic Data for Pre-training Generalist Policy Tian, Yang Yang, Yuyin Xie, Yiman Cai, Zetao Shi, Xu Gao, Ning Liu, Hangxu Jiang, Xuekun Qiu, Zherui Yuan, Feng Li, Yaping Wang, Ping Cai, Junhao Zeng, Jia Dong, Hao Pang, Jiangmiao Robotics Recent works explore how real and synthetic data contribute to Vision-Language-Action (VLA) models' generalization. While current VLA models have shown the strong effectiveness of large-scale real-robot pre-training, synthetic data has not previously demonstrated comparable capability at scale. This paper provides the first evidence that synthetic data alone can match the performance of the strongest $π$-dataset in pre-training a VLA model, revealing the substantial value of large-scale simulation. The resulting model also exhibits surprisingly zero-shot sim-to-real transfer on several challenging tasks. Our synthetic dataset, InternData-A1, contains over 630k trajectories and 7,433 hours across 4 embodiments, 18 skills, 70 tasks, and 227 scenes, covering rigid, articulated, deformable, and fluid-object manipulation. It is generated through a highly autonomous, fully decoupled, and compositional simulation pipeline that enables long-horizon skill composition, flexible task assembly, and heterogeneous embodiments with minimal manual tuning. Using the same architecture as $π_0$, we pre-train a model entirely on InternData-A1 and find that it matches the official $π_0$ across 49 simulation tasks, 5 real-world tasks, and 4 long-horizon dexterous tasks. We release the dataset and will open-source the generation pipeline to broaden access to large-scale robotic data and to lower the barrier to scalable data creation for embodied AI research. |
| title | InternData-A1: Pioneering High-Fidelity Synthetic Data for Pre-training Generalist Policy |
| topic | Robotics |
| url | https://arxiv.org/abs/2511.16651 |