[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-5de8c559-2ab5-44a7-b1d6-97cc8e499b25":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},"5de8c559-2ab5-44a7-b1d6-97cc8e499b25","蚂蚁灵波开源 LingBot-VLA 2.0：6 万小时数据 + 17 个品牌,把具身基座卷向跨构型","7 月 8 日,蚂蚁灵波科技宣布升级并开源新一代具身基座模型 LingBot-VLA 2.0,这是继今年 1 月 LingBot-VLA 1.0 之后的全面迭代。\n\n相比 1.0 版本,2.0 最直观的变化在数据规模和构型覆盖。在预训练阶段,模型融入了 6 万小时高质量真实物理数据,覆盖 17 个主流机器人品牌的 20 多种机器人构型。这背后解决的是具身智能的「跨构型泛化」老难题——以往 VLA 模型往往只能针对单一品牌或单一形态调优,换一个机器人就要从头微调。\n\n更重要的是自由度拓展:LingBot-VLA 2.0 新增对头部、腰部、末端执行器乃至移动底盘自由度的支持。这意味着同一个基座模型,既能操控固定臂的工业机械臂,也能驱动带移动底盘的服务机器人,甚至可以处理多自由度协同任务。\n\n蚂蚁灵波这次选择「开源」的时机并不偶然。VLA 类模型已进入「量产试水」阶段,从 Pi 0 到 RDT、NeuroVLA、HY-VLA,各家都在抢工厂与具身方案商的入口。开源一个跨 17 个品牌、能跨构型复用的基座,等于把自己做成「具身时代的 HuggingFace 候选」——通过占据工具链上游,去影响下游机器人的部署选型。\n\nLingBot-VLA 2.0 真正有意思的地方,不是数据量本身,而是它把「多构型 + 多自由度」压进同一个端到端基座模型里——这意味着具身智能正在走一条「粗统一、再细调」的路径,而不是永远靠堆叠专用模型。","https:\u002F\u002F36kr.com\u002Fnewsflashes\u002F3886479015555336","5e4fd3d1-9cb4-44a6-bae5-9ffb449c05c1",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"471c51be-e620-49df-bd6c-0b5504f53f00","ant-group",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"499f4b56-819d-49a3-9609-33e775143b86","multimodal",{"id":18,"name":19,"slug":19,"description":13,"color":13},"b1853a5a-d940-42b7-94f9-0488ee3f2cf7","new-model",{"id":21,"name":22,"slug":22,"description":13,"color":13},"b9bd9039-fcdb-41a8-b85b-fc1587def2b9","open-source","2026-07-08T06:30:00Z","2026-07-08T06:06:41.385390Z","2026-07-08T06:06:41.385399Z",true,"agent",2]