大摩重磅机器人年鉴：机器人"逃离工厂"，训练重点从“大脑”转向“身体”，边缘算力有望爆发

华尔街见闻2025-12-15

摩根士丹利最新指出，人工智能驱动的机器人正在经历从工厂车间向更广阔应用场景的历史性转移，训练重点从传统的认知能力转向物理操控能力，这一变化有望催生边缘计算需求的爆发式增长。12月15日，据硬AI消息，大摩在最新发布的《机器人年鉴（第二卷）》报告中指出，全球机器人行业正迎来两大关键转变：一是机器人应用场景从工厂向家庭、城市、太空等非结构化环境“逃逸”，二是训练重点从传统AI“大脑”（通用模型）转向“...

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focus of training shifts from \"brain\" to \"body\", and edge computing power is expected to explode","themeId":"","isJumpTheme":false,"ttsUrl":"https://static.tigerbbs.com/007cec7803c75a62a1951b1ac0a1507a","symbols_score_info":{"TSLA":2,"RR":2,"SERV":2,"TER":2},"content_text":"摩根士丹利最新指出，人工智能驱动的机器人正在经历从工厂车间向更广阔应用场景的历史性转移，训练重点从传统的认知能力转向物理操控能力，这一变化有望催生边缘计算需求的爆发式增长。12月15日，据硬AI消息，大摩在最新发布的《机器人年鉴（第二卷）》报告中指出，全球机器人行业正迎来两大关键转变：一是机器人应用场景从工厂向家庭、城市、太空等非结构化环境“逃逸”，二是训练重点从传统AI“大脑”（通用模型）转向“身体”（物理动作控制）。大摩指出，这一转变将驱动边缘算力需求爆发，实时推理芯片、模拟技术、机器人传感器等领域或成核心投资主线。报告强调，物理世界的复杂性（如抓取物体的力度控制、动态环境导航）正倒逼技术路线从“纯软件优化”转向“软硬协同”，而分布式边缘计算可能重塑全球算力基础设施格局。摩根士丹利预测，到2050年全球将售出14亿台机器人，这将推动边缘AI算力需求达到数百万个B200芯片当量，重塑全球计算基础设施的分布格局。机器人“逃离工厂”：从结构化牢笼到复杂现实世界传统工业机器人（Pre-AI Robotics）被局限于工厂的“结构化牢笼”：任务单一（如重复装配）、环境可控（固定产线）、无需感知与学习能力。大摩指出，AI赋能的新一代机器人正突破这一限制，开始进入家庭、农场、城市街道、深海甚至太空——例如自动驾驶汽车在拥挤路段导航、服务机器人在家庭中抓取物体、无人机在复杂地形巡检。报告以“抓取冰箱中的瓶子”为例说明物理世界的挑战：人类看似简单的动作，实则涉及手指精确位置、身体平衡调整、握力控制（过紧压碎、过松掉落）、环境湿度对摩擦力的影响等多重变量。大摩指出，这意味着机器人必须具备实时感知、动态决策与精细动作控制能力，而非仅依赖预设程序。训练范式转变：从“大脑”优化到“身体”控制报告称，早期机器人训练聚焦“大脑”（AI模型），如通用视觉-语言模型（VLM）的优化。但大摩强调，当前瓶颈已转向“身体”（物理动作执行），核心矛盾在于：人类本能的基础技能（如行走、抓取）对AI而言极其复杂（Moravec悖论），而这些技能无法通过互联网文本/图像数据简单习得。据大摩研究，与大语言模型主要训练文本和图像数据不同，机器人模型需要大量真实世界的物理操作数据，这使得数据收集和模型训练变得更加复杂且昂贵。该行指出，特斯拉、英伟达、谷歌等科技巨头正在通过远程操作、模拟训练和视频学习三种主要方式收集训练数据。远程操作（Teleoperation）：人类通过动作捕捉控制机器人，使其模仿行为。但该方法耗时、扩展性差，未来或逐步被替代。模拟训练（Simulation）：通过数字孪生在虚拟环境中无限复现复杂场景（如极端天气、障碍物），结合强化学习优化动作。游戏引擎公司（如Unreal Engine、Unity）已深度参与，NVIDIA的Omniverse平台正是基于其游戏GPU技术积累。视频学习（Videos）：从人类行为视频中提取动作模式（如YouTube视频），无需物理交互即可训练模型。谷歌DeepMind的Genie 3、Meta的V-JEPA 2等“世界模型”均采用类似思路，可预测物体运动轨迹与物理交互结果。边缘算力需求爆发：实时推理与分布式计算随着机器人“逃离工厂”后，云端中心化计算的延迟问题凸显（如自动驾驶需毫秒级决策），边缘算力成为刚需。大摩指出，边缘算力将呈现两大趋势：1. 专用边缘芯片普及英伟达的Jetson Thor是典型代表，作为边缘实时推理设备，每套件价格约3500美元，已被波士顿动力、亚马逊机器人等企业采用。其核心优势在于低功耗下实现高算力，满足机器人对实时性（如动态避障）的要求。2. 分布式推理网络特斯拉提出“机器人即算力节点”的构想：若全球部署1亿台具备2500 TFLOPS算力的机器人，50%利用率下可提供125000 ExaFLOPS算力，相当于700万颗NVIDIA B200 GPU（单颗18 PetaFLOPS）。这种分布式模式不仅降低对数据中心的依赖，还能通过机器人间协同提升整体效率。据摩根士丹利预测，到2030年全球机器人边缘计算需求将大幅增长，人形机器人、自动驾驶汽车、无人机等各类机器人形态都将贡献显著的算力需求。到2050年全球将售出14亿台机器人，将推动边缘AI算力需求达到数百万个B200芯片当量。","kind":"highlight","is_publish_news":true,"is_publish_highlight":true,"is_publish_live":false,"is_publish_wemedia":null,"editions":null,"column":"","sentiment":"1","news_tag":"","news_rank":0,"symbols":[],"gpt_button":0,"need_auth":false,"code":"91000000","status":"200"},"commentList":[],"isCommentEnd":true,"newsSizeData":{"likeSize":0,"commentSize":0,"repostSize":0,"favoriteSize":1,"likeStatus":false,"favoriteStatus":false},"APP":{"userAgent":"Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; 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