Abstract
Prevalent Vision-Language-Action (VLA) models are typically built upon Multimodal Large Language Models (MLLMs) and demonstrate exceptional proficiency in semantic understanding, but they inherently lack the capability to deduce physical world dynamics. Consequently, recent approaches have shifted toward World Models, typically formulated via video prediction; however, these methods often suffer from a lack of semantic grounding and exhibit brittleness when handling prediction errors. To synergize semantic understanding with dynamic predictive capabilities, we present InternVLA-A1. This model employs a unified Mixture-of-Transformers architecture, coordinating three experts for scene understanding, visual foresight generation, and action execution. These components interact seamlessly through a unified masked self-attention mechanism. Building upon InternVL3 and Qwen3-VL, we instantiate InternVLA-A1 at 2B and 3B parameter scales. We pre-train these models on hybrid synthetic-real datasets spanning InternData-A1 and Agibot-World, covering over 533M frames. This hybrid training strategy effectively harnesses the diversity of synthetic simulation data while minimizing the sim-to-real gap. We evaluated InternVLA-A1 across 12 real-world robotic tasks and simulation benchmark. It significantly outperforms leading models like pi0 and GR00T N1.5, achieving a 14.5\% improvement in daily tasks and a 40\%-73.3\% boost in dynamic settings, such as conveyor belt sorting.
Abstract (translated)
流行的视觉-语言-行动（VLA）模型通常基于多模态大型语言模型（MLLM），在语义理解方面表现出色，但它们本质上缺乏推断物理世界动态的能力。因此，最近的方法转向了通过视频预测构建的世界模型；然而，这些方法往往缺乏语义基础，并且在处理预测错误时表现得不够稳定。为了将语义理解与动态预测能力相结合，我们提出了InternVLA-A1模型。该模型采用统一的Transformer混合架构，协调三个专家模块进行场景理解、视觉前瞻性生成和动作执行。这三个组件通过统一的掩码自注意力机制无缝互动。基于InternVL3和Qwen3-VL，我们在20亿和30亿参数规模上实例化了InternVLA-A1模型。我们利用混合合成-真实数据集（涵盖InternData-A1和Agibot-World）对这些模型进行了预训练，包括超过5.33亿帧的数据。这种混合训练策略有效利用了仿真数据的多样性，并尽量减少从模拟到现实的差距。我们在12个实际机器人任务及模拟基准测试中评估了InternVLA-A1模型，它显著优于pi0和GR00T N1.5等领先模型，在日常任务上提高了14.5%，在动态场景（如传送带分拣）下提高了40%-73.3%。
URL
https://arxiv.org/abs/2601.02456
