在机器人学习如何"做人"这件事上，数据是核心要素——但它们大多诞生于实验室或特定"数据工厂"， 由专业团队设置模拟场景、反复演练、录制。然而，一千个家庭就有一千张不同的餐桌，饮食习惯不 同、物品摆放各异，甚至还随时可能有一只猫突然跳上来。 如何让具身智能更接近"人"？上海人工智能企业"穹彻智能"推出"口袋机采（RoboPocket）方案"——仅 由智能手机、App和轻巧机械手，即可实现轻量化、可控且高质量的数据采集。这意味着，在家庭、办 公室等各种真实场景，普通人也能成为机器人的"训练导师"。 "过去，具身智能数据采集大多通过雇佣专职的数采人员在数采工厂中完成，现在我们想让每个人能在 自己家里为具身智能采集有价值的数据。"穹彻智能研究科学家吕峻在接受本报记者采访时说，"这意味 着，数据采集从有限的物理空间，走向全社会。" 回顾自动驾驶的发展，当系统从实验室走向真实道路，数据也从少量、受控的采集，转向在真实场景中 的规模化沉淀，推动模型能力的快速演进。"具身智能与其他人工智能领域不同，没有现成的、海量 的、能给机器人学习的行为数据，我们需要找到一个方式记录人类世界千奇百怪的各种行为，来训练具 身智能的大模型。" ...

Core Insights - The core viewpoint of the articles is that the launch of RoboPocket by Qunchu Intelligent aims to address the critical bottleneck of high-quality and diverse data scarcity in the field of embodied intelligence, enabling ordinary users to participate in data collection [1][2]. Group 1: Product Launch and Features - Qunchu Intelligent has introduced the RoboPocket solution, which integrates professional-grade data collection capabilities into a smartphone, allowing for easy and accessible data gathering [1]. - RoboPocket utilizes built-in smartphone features such as RGB cameras, depth cameras (LiDAR), and sensors to replace traditional, expensive data collection equipment, facilitating a shift from "point collection" to "anytime, anywhere collection" [1][2]. Group 2: Industry Challenges and Solutions - The traditional data collection methods face a "trilemma" of data quality, portability, and post-processing efficiency, where improving one aspect often compromises another [2]. - RoboPocket is described as an "intelligent hub" that not only records data but also provides real-time diagnostics and guidance to users, enhancing the quality and value of the collected data [2]. Group 3: Future Aspirations and Impact - The company aims to establish a stable source of usable data assets from the real world to drive rapid model iteration and cost reduction, with plans to implement standardized processes across various sectors such as pharmacies and hotels [3]. - Data collected through RoboPocket has already demonstrated the ability to train models that can perform tasks on industrial cameras and robotic systems, indicating the feasibility of low-cost data collection [2].