Qwen-RobotManip 技術報告：對齊機制釋放機器人操作基礎模型的規模效應

摘要

基礎模型在語言與多模態領域通過在統一表徵下對齊異質數據並進行大規模訓練，實現了強大的泛化能力。本報告探討此擴展方法是否可應用於機器人操作以達成真正的泛化。這極具挑戰性，因為與文本不同，操作數據本質上具有異質性、收集成本高昂且多樣性狹窄，使得對齊與規模化同時面臨困難。我們提出Qwen-RobotManip，一種基於Qwen-VL的可泛化視覺-語言-動作基礎模型。Qwen-RobotManip在操作的表徵、運動與行為維度上引入統一對齊框架，使大規模多源訓練協調一致而非相互衝突。此對齊能力進而使Qwen-RobotManip能夠吸收此前訓練機制無法支撐規模的操作數據。一條人機合成流水線將15個平台的以自我為中心的手部演示轉化為機器人軌跡，並通過嚴格的整理流程協調異質數據集。僅使用開源數據集與人類視頻而無需專有數據收集，Qwen-RobotManip構建了約38,100小時的預訓練語料庫，並展現出新興泛化能力，包括零樣本指令遵循、對擾動的魯棒性、反應性錯誤恢復以及跨本體遷移。我們發現標準基準無法捕捉預訓練質量，因此採用分佈外設定，包括RoboCasa365、LIBERO-Plus、EBench、RoboTwin-Clean2Rand、RoboTwin-IF與RoboTwin-XE。Qwen-RobotManip在所有分佈外設定中大幅優於此前最先進模型（包括π0.5），在RoboChallenge中以20%的相對提升排名第一，並在包括AgileX ALOHA、Franka、UR及ARX在內的實體機器人平台上得到驗證。

English

Foundation models in language and multimodality achieve strong generalization by aligning heterogeneous data under a unified formulation and training at scale. In this report, we investigate whether this scaling recipe can be applied to robotic manipulation to achieve genuine generalization. This is challenging because, unlike text, manipulation data is heterogeneous by nature, expensive to collect, and narrow in diversity, making alignment and scale simultaneously difficult. We present Qwen-RobotManip, a generalizable Vision-Language-Action foundation model built on Qwen-VL. Qwen-RobotManip introduces a unified alignment framework across the representation, motion, and behavioral dimensions of manipulation, making large-scale multi-source training coherent rather than conflicting. This alignment capability in turn enables Qwen-RobotManip to absorb manipulation data at a scale that prior training regimes could not sustain. A human-to-robot synthesis pipeline converts egocentric hand demonstrations into robot trajectories across 15 platforms, and a rigorous curation pipeline harmonizes heterogeneous datasets. Using only open-source datasets and human videos without proprietary data collection, Qwen-RobotManip constructs a ~38,100-hour pretraining corpus and exhibits emergent generalization capabilities, including zero-shot instruction following, robustness to perturbations, reactive error recovery, and cross-embodiment transfer. We find that standard benchmarks fail to capture pretraining quality and instead adopt OOD settings including RoboCasa365, LIBERO-Plus, EBench, RoboTwin-Clean2Rand, RoboTwin-IF, and RoboTwin-XE. Qwen-RobotManip substantially outperforms prior state-of-the-art models, including π0.5, across all OOD settings, ranks 1st in RoboChallenge with a 20% relative improvement, and is validated on real-robot platforms including AgileX ALOHA, Franka, UR, and ARX.