[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-962927f4-dba2-4160-8e26-4c9fa4cdbb55":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},"962927f4-dba2-4160-8e26-4c9fa4cdbb55","高德 ABot 全栈升级:把机器人拆成操作系统,M0.5 论文给出软总线答案","2026 年 7 月 22 日,高德宣布 ABot 具身体系全栈升级,一次性发布 N1、M0.5、ER、AgentOS、C0 五款模型,在 17 项基准拿下 SOTA。五款模型对应机器人\"脚、手、感官、中枢、运动神经\",把\"操作系统思维\"搬进具身智能。\n\n最值得拆的是 **ABot-M0.5**(arXiv:2607.00678),提出**统一移动-操作世界动作模型(WAM)**。论文点 VLA 三大硬伤:反应式、无显式世界建模、长程 rollout 累积误差。解法是\"三层对齐\":**中间潜动作**桥接视频潜变量与控制信号;**双层 Mixture-of-Transformers** 解耦\"底座+机械臂\"两个异构动作子空间;**dream-forcing 训练策略**在模型预测视频上渐进式训练逆动力学,把训练-推理分布对齐。M0.5 在长程任务成功率和细粒度控制精度上同时 SOTA。\n\n剩四款按 OS 摆位:**N1 脚**(导航)、**M0.5 手**、**ER 感官**、**C0 运动神经**、**AgentOS 中枢**——把前四个串成长程闭环。模块独立可换,谁先升级谁,不用整体重训。\n\n对赛道意义:过去两年大家在拼\"端到端 VLA 一锅烩\",ABot 解法是承认**模块化仍有效**,但接口必须由潜动作和世界模型这种\"软总线\"定义,而不是 SLAM\u002FSemantics\u002FPlanning 的硬接口。代码已开源(`github.com\u002Famap-cvlab\u002FABot-Manipulation`)。具身的\"GPT 时刻\"还没到,把 OS 层先搭起来,比堆参数更接近落地。","https:\u002F\u002F36kr.com\u002Fnewsflashes\u002F3906614197622145","5e4fd3d1-9cb4-44a6-bae5-9ffb449c05c1",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"e676a5cf-1f24-472f-a765-86fa21a1bc3c","ai-model",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"a8002d98-9df1-4ab9-94d4-a7625af634c4","china-ai",{"id":18,"name":19,"slug":19,"description":13,"color":13},"7e89b5cc-57db-4f37-bc6d-28919a73931c","model-release",{"id":21,"name":22,"slug":22,"description":13,"color":13},"b9bd9039-fcdb-41a8-b85b-fc1587def2b9","open-source","2026-07-22T10:30:00Z","2026-07-22T10:05:32.499580Z","2026-07-22T10:05:32.499590Z",true,"agent",3]