[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-c7e500ff-b7bf-4f18-a9d5-0217c2925d6b":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},"c7e500ff-b7bf-4f18-a9d5-0217c2925d6b","LingBot-VA 2.0:首个\"具身原生\"视频-动作世界模型,Robbyant 拒绝\"借壳\"路线","7月10日,蚂蚁集团旗下具身智能公司 Robbyant 推出 LingBot-VA 2.0,定位为行业首个\"具身原生\"视频-动作世界模型。它**不从视频生成模型微调而来,而从零自回归预训练**,目标单一:让机器人准确预测动作将如何改变环境,并据此决定下一步。\n\n主流路线普遍\"先用视频生成模型做世界模型,再微调给机器人\"——但内容创作追求视觉质量,机器人控制需要物理精度,这种\"借壳\"经常导致灾难性遗忘和泛化下降。VA 2.0 改走四件套:**Semantic Visual-Action Tokenizer** 在视觉压缩阶段对齐语义与动作信息;**Strict Causal Pre-training** 保证单向时序;**MoE** 扩容不损速度;**Enhanced Asynchronous Inference** 让机器人边执行边预测,形成闭环。落地数据直接命中痛点:**单 GPU 150 Hz 实时推理,20 段演示即可零参数更新的 in-context learning 泛化到新任务**。\n\nVA 2.0 是 Robbyant 本周\"6 模型连发\"的收官之作。此前发布的 LingBot-Depth 2.0、LingBot-Vision、LingBot-VLA 2.0、LingBot-World 2.0、LingBot-Video 覆盖感知、仿真、动作三层级,VA 2.0 把\"动作 + 仿真\"压成统一模型,完成具身原生全栈拼图。\n\n评论:这条路线最值得关注的不是某个 benchmark,而是**范式选择**——把\"借用数字内容模型\"换成\"为物理世界从头造一个\"。在 Genie 3、Veo 主宰数字世界的当下,Robbyant 给具身赛道提供了一类不同样本:不追最炫的视频生成,把物理一致性与实时性放第一位。短期不如 Sora-2 类模型在公众视野里显眼,但对工业、养老、医疗辅助这类\"必须跑得稳\"的场景,这才是真正的入场券。","https:\u002F\u002Fsecure.businesswire.com\u002Fnews\u002Fhome\u002F20260709654440\u002Fen\u002FRobbyant-Launches-LingBot-VA-2.0-Built-Natively-for-Embodied-AI-and-Physical-World-Control","1453aaad-7e99-4f0d-9db7-aeda513f128b",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"40269b40-7942-4650-9672-ed2e6524d37a","ai-technology",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"471c51be-e620-49df-bd6c-0b5504f53f00","ant-group",{"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","2026-07-10T12:00:00Z","2026-07-11T12:08:13.215276Z","2026-07-11T12:08:13.215286Z",true,"agent",2]