Qwen-VLA：统一跨任务、环境与机器人形态的视觉-语言-动作建模

Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments

AI 导读

Qwen-VLA是一个统一的具身基础模型，将Qwen的视觉-语言建模从感知、理解与推理扩展至连续动作和轨迹生成。它通过基于DiT的动作解码器实现，使用包含机器人操作轨迹、人类第一人称示范、仿真及导航数据等在内的大规模数据进行联合预训练。为支持多种平台，引入了感知载体感知的提示条件机制，并将操作、导航与轨迹预测统一到一个框架中。实验显示，Qwen-VLA-Instruct在多个基准上表现优异，例如在LIBERO达到97.9%，在真实世界ALOHA实验中平均分布外成功率为76.9%。

推荐理由

Qwen-VLA 让一个模型同时搞定操作、导航和轨迹，在具身智能统一化上迈出了关键一步。虽然还停在实验室阶段，但 97.9% LIBERO 和真实世界泛化结果证明这条路走得通，做机器人的值得认真读。

正文 · 原文

Embodied intelligence is often studied through specialized models for individual tasks such as manipulation or navigation, resulting in fragmented capabilities and limited generalization across tasks, environments, and robot embodiments. In this work, we study whether heterogeneous embodied decision-making problems can be unified within a single vision-language-action model. We present Qwen-VLA, a unified embodied foundation model that extends Qwen's vision-language modeling stack from perception, understanding, and reasoning to continuous action and trajectory generation through a DiT-based action decoder. Qwen-VLA is trained with a large-scale joint pretraining recipe over diverse data sources, including robotics manipulation trajectories, human egocentric demonstrations, synthetic simulation data, vision-and-language navigation data, trajectory-centric supervision, and auxiliary vision-language data. To support multiple robot platforms, we introduce embodiment-aware prompt conditioning, where robot-specific textual descriptions specify the current embodiment and control convention. We further cast manipulation, navigation, and trajectory prediction into a unified action-and-trajectory prediction framework, enabling transferable visual grounding, spatial reasoning, and continuous action generation across robot morphologies, task families, and environments. Experiments on manipulation, navigation, and trajectory-centric benchmarks show consistent multi-task performance and out-of-distribution generalization under variations in scene layout, background, lighting, object configuration, and robot embodiment. Qwen-VLA-Instruct achieves 97.9% on LIBERO, 73.7% on Simpler-WidowX, 86.1%/87.2% on RoboTwin-Easy/Hard, 69.0% OSR on R2R, 59.6% SR on RxR, 76.9% average OOD success in real-world ALOHA experiments, and 26.6% zero-shot success on DOMINO dynamic manipulation.