重磅︱国地中心发布首个权威认证百万规模异构数据集—“白虎”，打造具身智能机器人训练数据新标杆！

Core Viewpoint - The article emphasizes the strategic importance of data in the advancement of robotics towards autonomy and generalization, highlighting the launch of the "White Tiger" dataset to address data bottlenecks in the industry [1][2][35]. Group 1: Dataset Characteristics - The "White Tiger" dataset is the world's first heterogeneous robot dataset exceeding one million samples, collected from real-world applications and covering various humanoid and robotic platforms [1][2]. - The dataset has received official certification from the China Academy of Information and Communications Technology, marking it as the first authoritative certified dataset in the field of embodied intelligence in China [2]. - It includes data from multiple robot platforms, such as the Qianlong, PortaGrip, and others, with specific proportions of data representation from each platform [7]. Group 2: Data Collection and Quality - The dataset was created to overcome the challenges of isolated data collection and unstandardized formats, establishing a unified data collection system adaptable to various robotic platforms [5]. - A comprehensive data quality control system was implemented to ensure high standards in the dataset, enhancing its reliability for training and evaluation [28]. Group 3: Application Scenarios and Tasks - The "White Tiger" dataset is structured around five major application scenarios, significantly improving the robots' environmental perception and cross-scenario generalization capabilities [8]. - It features a multi-dimensional task system that allows for the structured breakdown of tasks into atomic skills, facilitating the training of robots across various scenarios [12]. Group 4: Interaction and Skill Development - The dataset includes a diverse range of interaction targets, covering over a hundred types of real objects, which enhances the robots' manipulation learning boundaries [14]. - It systematically labels over a hundred atomic skills, providing essential operational representations for complex task behavior understanding and generation [26]. Group 5: Temporal Data and Behavior Modeling - The dataset records task execution processes across short, medium, and long time scales, supporting hierarchical behavior modeling from low-level action control to high-level task planning [16]. Group 6: Future Implications - The "White Tiger" dataset aims to establish a robust foundation for general intelligence in robotics, addressing key challenges in data volume, engineering standards, application breadth, and intelligence depth [35]. - The initiative invites collaboration across industry, academia, and research sectors to foster an open ecosystem for advancing general robotics towards higher intelligence and stronger generalization capabilities [35].