点击下方 卡片 ，关注" 具身智能 之心 "公众号 作者丨 Pei Yang等 编辑丨具身智能之心 本文只做学术分享，如有侵权，联系删文 >> 点击进入→ 具身智能之心 技术交流群 更多干货，欢迎加入国内首个具身智能全栈学习社区 ： 具身智能之心知识星球 (戳我) ， 这里包含所有你想要的。 在 embodied AI 领域，视觉 - 语言 - 动作（VLA）模型与世界模型的发展虽展现出通用自主能力的巨大潜力，却始终受限于核心瓶颈——大规模、多样化机器人训 练数据的稀缺。现有解决方案要么依赖成本高昂的真实机器人数据采集，要么通过简单叠加机器人部件编辑第一视角人类视频，均无法应对第三人称视频中的全身 复杂动作、动态背景与严重遮挡问题。 新加坡国立大学 Show Lab 提出的 X-Humanoid 框架 ，以 "数据合成 - 模型适配 - 大规模生成" 为核心逻辑，首次实现第三人称人类视频到类人机器人视频的高质量 转化，为机器人训练提供了全新的数据生成范式。 论文题目：X-Humanoid: Robotize Human Videos to Generate Humanoid Videos at Scale 项 ...

Core Insights - The article discusses the rising trend of using household chore videos as high-value training data for humanoid robots, with companies like Encord, Micro1, and Scale AI actively purchasing this content [7][10][19]. Industry Overview - The robotics sector is currently experiencing significant investment, with venture capital in the field reaching $12.1 billion this year alone [10]. - There is a notable data scarcity issue in the robotics industry, as robots require real-world training data that is not readily available like internet datasets for language models [11]. Data Sources - Training data for robots can be sourced from two main paths: real-world data and synthetic data [12]. - Real-world data can be collected through precise equipment that remotely controls robots, capturing detailed physical interactions [12][14]. - Synthetic data is generated in virtual environments, allowing for the creation of numerous action variations at a lower cost [16]. Data Processing Strategies - Companies are combining real and synthetic data to address the scarcity of quality training data, utilizing a small amount of real-world data alongside large volumes of synthetic data [18]. - Encord has reported a fourfold increase in data processing this year compared to last year, with high compensation for high-skill task videos reaching $150 per hour [19]. Market Demand - Demand for training data is coming from companies like Physical Intelligence and Boston Dynamics [22]. - Some startups are even advertising for users to film household chores for as little as $10 to $20 per hour [23]. Data Availability Challenges - Despite efforts from various companies, high-quality training data remains scarce, with the largest available datasets only amounting to about 5,000 hours, which is insufficient for training needs [26].