← 研踪 PaperTrailCVF · CVPR 2026:cvf:81212ba3a4f2708dbdafcfdc3336ccd2 · 记录 #1
InternData-A1: Pioneering High-Fidelity Synthetic Data for Pre-training Generalist Policy
Tian, YangYang, YuyinXie, YimanCai, ZetaoShi, XuGao, NingLiu, HangxuJiang, XuekunQiu, ZheruiYuan, FengLi, YapingWang, PingCai, JunhaoZeng, JiaDong, HaoPang, Jiangmiao 来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 01:35
摘要
Recent work explores how real and synthetic data contribute to VLA model generalization. While the \pi-series model has 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 \pi-dataset in pre-training a VLA model, revealing the substantial value of large-scale simulation.The resulting model also exhibits surprisingly strong 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 flexible task assembly, long-horizon skill composition, and heterogeneous embodiments with minimal manual tuning.Using the same architecture as \pi_0, we pre-train a model entirely on InternData-A1 and find that it matches the official \pi_0 across 49 simulation tasks, 5 real-world tasks, and 4 long-horizon dexterous tasks.We will open-source both the dataset and the generation pipeline to broaden access to large-scale robotic data and to lower the barrier to scalable data creation for embodied AI research.
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记录 #1 · 2026 · 最新观测版本
InternData-A1: Pioneering High-Fidelity Synthetic Data for Pre-training Generalist Policy
Recent work explores how real and synthetic data contribute to VLA model generalization. While the \pi-series model has 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 \pi-dataset in pre-training a VLA model, revealing the substantial value of large-scale simulation.The resulting model also exhibits surprisingly strong 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 flexible task assembly, long-horizon skill composition, and heterogeneous embodiments with minimal manual tuning.Using the same architecture as \pi_0, we pre-train a model entirely on InternData-A1 and find that it matches the official \pi_0 across 49 simulation tasks, 5 real-world tasks, and 4 long-horizon dexterous tasks.We will open-source both the dataset and the generation pipeline to broaden access to large-scale robotic data and to lower the barrier to scalable data creation for embodied AI research.
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