Core Insights - Leju Intelligent has donated its LET dataset to the OpenLoong open-source community, marking a significant step in the development of humanoid robots in China [1][4] - The LET dataset is a comprehensive collection of real-world data, exceeding 60,000 minutes, covering various operational scenarios across multiple industries [2][3] Group 1: Dataset Characteristics - The LET dataset is constructed to represent real operational scenarios for full-sized humanoid robots, encompassing industrial, commercial retail, and daily life environments [2] - It includes 31 tasks and 117 atomic skills, forming a clear task system that supports multi-scenario, multi-step, and multi-objective learning and reasoning for robots [2] Group 2: Industry Challenges and Solutions - The humanoid robotics industry faces challenges such as fragmented data sources and inconsistent formats, which hinder data quality and collaborative efficiency [3] - The donation of the LET dataset aims to address these issues by providing a standardized, high-quality data resource that enhances data circulation and value in the humanoid robotics sector [3] Group 3: Ecosystem Development - The LET dataset will be continuously maintained and updated under the Open Atom Open Source Foundation, contributing to a systematic resource for real-world data in the industry [4] - The integration of the LET dataset into the OpenLoong community will facilitate deeper research in task modeling, skill learning, and strategy validation, while providing high-quality samples for performance verification [4]

Core Insights - Leju Intelligent has donated its LET dataset to the OpenLoong open-source community, enhancing the data resources available for humanoid robot development in China [1] - The LET dataset is significant for its large scale, structured format, and diverse scenarios, marking a new phase in the OpenLoong data ecosystem [1] Group 1 - The LET dataset covers three major areas: industrial, commercial retail, and daily life, including six categories of real production and service environments such as automotive factories and logistics [2] - It includes 31 tasks and 117 atomic skills, forming a clear task system suitable for training robots under various conditions [2] - The dataset records multimodal information, including RGB, depth, joint states, and end-effector states, achieving over 90% data consistency [2]

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].