具身智能困于数据孤岛，京东为何入局第一视角数据赛道？

新京报04-17

从实验室里的翻跟头、定点动作演示，到真实场景中稳定完成复杂任务，具身智能的商业化落地，始终被一道核心门槛牢牢卡住——训练数据的规模化供给与跨主体流通。这一全行业的共性痛点，正在引发新一轮的技术路线探索与产业布局调整。　　4月16日，京东对外公布行业首个具身智能数据全链路基础设施，成为继英伟达等国际厂商之后，又一家从数据底层切入行业的头部企业。在发布会后的媒体群访中，京东集团副总裁、京东云基础云...

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Why did JD.COM enter the first perspective data track?","themeId":null,"isJumpTheme":false,"ttsUrl":null,"symbols_score_info":{"89618":0.6,"KJD":0.6,"09618":1.95,"JD":1.95},"content_text":"从实验室里的翻跟头、定点动作演示，到真实场景中稳定完成复杂任务，具身智能的商业化落地，始终被一道核心门槛牢牢卡住——训练数据的规模化供给与跨主体流通。这一全行业的共性痛点，正在引发新一轮的技术路线探索与产业布局调整。　　4月16日，京东对外公布行业首个具身智能数据全链路基础设施，成为继英伟达等国际厂商之后，又一家从数据底层切入行业的头部企业。在发布会后的媒体群访中，京东集团副总裁、京东云基础云业务部负责人龚义成、京东具身智能研究员李一航详解了其技术路线与布局逻辑，而这一系列动作，本质上是国内具身智能产业在数据瓶颈下的集体探索缩影，折射出行业早期阶段的生态探索。　　全行业共性堵点：遥操作模式触顶，数据供给陷入死循环　　当前全球具身智能产业仍处于商业化落地的前夜，一个无法回避的行业现状是：绝大多数机器人产品仍停留在演示阶段，只能在限定环境中完成预设动作，一旦进入真实场景，泛化能力便大幅下滑，无法稳定解决实际问题。　　“具身机器人的大脑智能化程度偏低，核心问题就是具身模型训练缺乏规模化高质量数据集。”龚义成在采访中直言，行业沿用多年的遥操作数据采集模式，已经走到了规模化的天花板，这是全行业共同面对的瓶颈。　　所谓遥操作，即由专人通过手柄等设备操控机器人，同步采集抓拿取放等动作数据，用于模型训练。这一模式存在两个天然的、无法突破的短板。其一，数据规模无法起量；其二，数据孤岛问题无解。　　2026年被业内多位从业者定义为“具身数据元年”，本质上是行业对数据的需求，已经从“补充项”变成了“生存项”。面对遥操作模式的天花板，全球范围内的头部企业与科研机构，均开始转向人类第一视角（Ego）数据的技术路线探索，即通过采集人类在真实场景中完成动作的第一视角视频数据，训练具身模型，京东也是这一路线的入局者之一。　　龚义成表示，人类第一视角真实场景数据，是解决具身模型规模化训练的重要一环，但这条路线的落地，并非简单的视频采集，而是需要解决采集设备、数据质量、配套基础设施三大全行业空白，这也是所有入局者共同面对的挑战。　　“既要在真实场景下实现规模化数据采集，同时还要兼顾成本与数据质量。”龚义成介绍，京东为此自研了第一视角采集设备JoyEgoCam，核心是解决采集环节的落地难题。　　数据质量也是全行业普遍踩坑的环节，大量企业盲目堆高数据采集时长，却忽略了质量管控，最终导致数据无法转化为模型能力。龚义成举例，在模型试验中存在这样的情况，比如用1000小时高质量数据训练，模型任务准确率很高，但后续再喂入1000小时低质量数据后，模型准确率反而下降。　　龚义成在采访中直言，低质量数据对模型不仅没有价值，甚至会产生反作用。李一航透露，京东将从三方面保障数据质量：一是采集设备参数贴近人类视觉特性，保证原始数据质量；二是尽可能地保证多样性的采集，并开展人工与算法质检与合规检查，剔除不合格、不多样的数据；三是通过自研数据处理管线，将采集数据转化为高精度、可直接用于模型训练的标注数据。　　龚义成表示，京东已搭建了从数据采集、清洗、自动化标注到模型训练的全链路基础设施，其中包括业内首家支持具身智能千卡级LeRobot开源训练框架的AI开发平台，核心就是解决大规模第一视角数据的落地应用难题。　　值得注意的是，京东并非这条路线的唯一入局者。英伟达今年推出的EgoScale已经验证了第一视角数据的有效性，国内多家机器人企业、科研机构也已启动相关布局，行业路线分化已经显现，但所有路线都仍处于早期验证阶段，尚未有哪条路线被证明可以完全支撑具身智能的规模化落地。　　数据孤岛制约发展，核心优势则在于场景丰富　　目前行业内的主流模式，是各家机器人企业自建数据采集团队、封闭自有数据集，数据仅内部使用。　　这也是京东此次布局的核心落点：放弃单一数据提供商的定位，转向搭建开放的行业生态。“京东不可能把所有场景的数据都采集完，这件事必须依赖全行业的合作伙伴一起完成。”龚义成明确表示，其核心目标是联合行业共建具身智能数据联盟，打破数据孤岛，通过数据的开放流通，降低全行业的研发成本。　　此前行业内的数据集开放，多为科研机构的开源项目，或企业的有限度开源，尚未形成成熟的商业化流通体系。但需要正视的是，这一模式能否跑通，仍存在大量未知数：数据的知识产权归属、合规流通规则、定价体系、隐私安全保障，均是行业尚未形成共识的难题。　　尽管行业热度持续攀升，资本与产业投入不断加大，但多位从业者在采访中坦言，全球具身智能产业仍处于早期发展阶段。　　谈到规模化落地，李一航表示，在物流等对效率、时效要求极高的场景中，具身智能想要真正落地，任务成功率至少要达到99%，甚至99.9%；而在家庭、导购、接待等泛家庭场景中，只要能解决90%的问题，即便达不到极高的成功率，也已经具备落地应用的价值。　　行业内一直存在不同声音：有观点认为，当前行业过分关注数据规模，忽略了模型架构、算法创新的核心价值；也有观点质疑，大语言模型领域的Scaling Law（缩放定律），在具身智能领域是否依然适用。　　李一航表示，在具身方向远没有到Scaling Up（规模化）非常强的地步，数据量级和大语言模型、一般多模态模型相比差很多量级，确实能够观察到随着数据量级的扩充，具身模型泛化能力还在指数级别的提升，多条路线仍有极大探索空间。　　而在全球产业格局上，龚义成判断，目前行业整体处于比较早期的阶段，中国的核心优势在于场景丰富和创新性落地，作为制造业强国拥有丰富多样的应用场景，同时在机器人本体、硬件、小脑等方面具备较强实力。　　此外，人才争夺与技术对就业的影响也是行业无法回避的议题。当前行业对人才需求迫切。龚义成透露，目前团队在具身智能相关领域招聘人才时，核心要求是必须对AI有深刻理解，是AI的使用者与拥抱者，能够熟练用好AI工具，这也是行业普遍的人才需求趋势。（文章来源：新京报）","kind":"news","is_publish_news":true,"is_publish_highlight":false,"is_publish_live":false,"is_publish_wemedia":null,"editions":null,"column":"","sentiment":"0","news_tag":"","news_rank":0,"isVideo":false,"video":null,"symbols":[],"gpt_button":1,"need_auth":false,"need_login_tip":false,"code":"91000000","status":"200"},"commentList":[],"isCommentEnd":true,"newsSizeData":{"likeSize":0,"commentSize":0,"repostSize":0,"favoriteSize":0,"likeStatus":false,"favoriteStatus":false},"APP":{"userAgent":"Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)","isDev":false,"isTTM":false,"tenantId":"TBCN","deviceId":"web-server-community-laohu8-v3","version":"4.42.0","shortVersion":"4.42.0","platform":"web","vendor":"web","appName":"laohu8","isIOS":false,"isAndroid":false,"isTiger":false,"isTHS":false,"isWeiXin":false,"isWeiXinMini":false,"isWeiBo":false,"isQQ":false,"isBaiduSwan":false,"isBaiduBox":false,"isDingTalk":false,"isToutiao":false,"isOnePlus":false,"isHuaWei":false,"isXiaomi":false,"isXiaomiWebView":false,"isOppo":false,"isVivo":false,"isSamsung":false,"isMobile":false},"href":"/m/news/2628200621","isCrawlerRequest":true}