开源发布 | 乐聚 LET 数据集正式捐赠至 OpenLoong 开源社区，遵循国地中心统一数据标准

Core Viewpoint - The article emphasizes the importance of high-quality, multi-modal, and structured data in advancing humanoid robot technology and its applications, highlighting the donation of the LET dataset to the OpenLoong open-source community as a significant step towards building a unified data infrastructure in the industry [1][11]. Group 1: LET Dataset Overview - The LET dataset, constructed by Leju Intelligent and its partners, is one of the few full-size humanoid robot datasets in China, covering real operational scenarios with over 60,000 minutes of data [2]. - The dataset encompasses diverse task scenarios across three main fields: industrial, commercial retail, and daily life, including six categories such as automotive factories and logistics, with a clear task system comprising 31 tasks and 117 atomic skills [4]. Group 2: Data Collection and Technology Innovation - The dataset records both head and dual-wrist visual streams, providing multi-modal information such as RGB, depth, joint states, and end-effector states, achieving data consistency over 90% through advanced frame grouping technology [5][6]. - Complex tasks are broken down into clearly defined atomic action steps, accompanied by semantic labels, facilitating model understanding of task structures and action logic, thus enhancing behavior understanding and skill learning [7]. Group 3: Building a Trustworthy Data Standard System - The humanoid robot industry faces challenges such as fragmented data sources and inconsistent formats, necessitating a systematic data standard to enhance data quality and model capabilities [12]. - The National and Local Joint Innovation Center for Humanoid Robots has established a comprehensive standard system covering data collection, processing, quality review, and version management, ensuring data quality and usability [14]. Group 4: OpenLoong Community and Future Prospects - The donation of the LET dataset to the OpenLoong community enriches the repository of real-world data, promoting deeper research in task modeling, skill learning, and strategy validation [11][24]. - OpenLoong aims to create a shared data framework to standardize data organization and reuse, with the LET dataset serving as a representative training sample for the industry [20][26].