[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-00c0b8cc-729a-4daa-b529-ee4313621458":3},{"id":4,"title":5,"summary":6,"original_url":7,"source_id":8,"tags":9,"published_at":23,"created_at":24,"modified_at":25,"is_published":26,"publish_type":27,"image_url":13,"view_count":28},"00c0b8cc-729a-4daa-b529-ee4313621458","阿里 Qwen-Robot Suite 三连发：把「导航-操作-世界模型」打通成一套具身栈","阿里通义千问团队 6 月 16 日发布 Qwen-Robot Suite，一口气放出三个面向具身智能的基础模型：\n\n- Qwen-RobotNav 把\"指令跟随、点目标导航、目标搜索、目标跟踪、自动驾驶\"五项任务塞进一个模型，用 1560 万样本训练，在 VLN-CE RxR 拿到 76.5%、EVT-Bench 跟踪任务 90%；\n- Qwen-RobotManip 针对跨本体这一老大难（Franka 关节角 vs ALOHA 末端位姿 vs 人形全身坐标），对齐 3.81 万小时开源与人类视频数据，RoboChallenge Table30-v1 以 20% 优势登顶；\n- Qwen-RobotWorld 是最激进的语言条件视频世界模型，把\"拿起红杯子给花浇水\"统一成跨本体可执行指令，860 万视频-文本对、2 亿帧，覆盖 1300+ 技能、20+ 形态、14 种机械臂，在 EWMBench、DreamGenBench 双榜第一，物理一致性近乎满分。\n\n这套组合的真正信号不是三个独立 SOTA，而是\"统一栈\"野心：同一套基座既能驱动四足\u002F轮式移动平台，也能在机械臂、人形机器人、自动驾驶车上复用。比起 DeepMind、NVIDIA Cosmos、Figure、Physical Intelligence 各自只攻导航或操作的路线，阿里选择横向铺开，再借云、芯片、阿里云企业客户的渠道下沉。但要警惕\"demo 到工厂\"的鸿沟——仿真榜单到真实部署还要跨过传感器噪声、长期漂移和长尾场景。\n\n对中国玩家而言，意义不止模型本身：它把 Qwen 从\"聊天+视觉\"推到\"物理动作\"，让具身 AI 的 OS 层有了国产开源选项。短期看是技术发布，长期看是阿里把云、芯片、模型、机器人企业客户捆成一条线的生态卡位。","https:\u002F\u002Fdecrypt.co\u002F371357\u002Falibaba-qwen-robot-operating-system-robot-economy","ccc562ce-84df-4c08-a4be-e41aa105d7af",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"a8002d98-9df1-4ab9-94d4-a7625af634c4","china-ai",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm",{"id":18,"name":19,"slug":19,"description":13,"color":13},"499f4b56-819d-49a3-9609-33e775143b86","multimodal",{"id":21,"name":22,"slug":22,"description":13,"color":13},"b1853a5a-d940-42b7-94f9-0488ee3f2cf7","new-model","2026-06-17T02:00:00Z","2026-06-17T02:27:35.111547Z","2026-06-17T02:27:35.111566Z",true,"agent",5]