Core Viewpoint - The article emphasizes the importance of high-quality, large-scale, and diverse datasets for advancing embodied intelligence, highlighting the release of the "10Kh RealOmni-Open DataSet" by JianZhi Robotics as a significant milestone in the industry [1][4][16]. Dataset Overview - The "10Kh RealOmni-Open DataSet" consists of over 10,000 hours of data and nearly one million clips, making it the largest and most generalized open dataset in the field [1][4]. - The dataset focuses on 10 common household tasks, ensuring that each skill has over 10,000 clips, which enhances both the scale and depth of skills covered [4][5]. Data Quality and Specifications - The dataset features high-quality recordings with a resolution of 1600x1296 pixels and a frame rate of 30 fps, ensuring clarity and detail in the captured actions [4][5]. - It achieves centimeter-level trajectory precision, with advanced IMU hardware and cloud reconstruction techniques enhancing the accuracy to sub-centimeter levels [4][12]. Skill and Task Coverage - The dataset prioritizes tasks that can be performed with one hand in real scenarios, with 99.2% of the clips involving "two-handed, long-range tasks," providing a realistic representation of household activities [5][7]. - The average clip length is 1 minute and 37 seconds, capturing the complete process of tasks rather than static snapshots, which aids in understanding action logic and causality [5][7]. Data Collection Methodology - The data was collected from 3,000 real households, ensuring a rich variety of scenarios and natural human interactions, addressing the limitations of traditional data collection methods [7][9]. - JianZhi Robotics employs a comprehensive data production chain, allowing for rapid accumulation of data, with nearly one million hours collected in just two months [9][11]. Technological Infrastructure - The Gen DAS Gripper is a key component in the data collection process, enabling quick deployment without the need for extensive site preparation [11]. - The Gen Matrix data platform processes and cleans the collected data, achieving high precision in trajectory reconstruction and synchronization of heterogeneous data sources [13]. Future Directions - The open-sourcing of this dataset is seen as a way to accelerate innovation in embodied intelligence by filling data gaps, standardizing formats, and lowering research barriers [16]. - JianZhi Robotics plans to continue enhancing its data infrastructure and releasing more beneficial datasets and services, fostering a positive cycle of data sharing, model optimization, and practical application [16].

Core Viewpoint - The article discusses the release of the largest and most generalized open dataset for embodied intelligence, "10Kh RealOmni-Open DataSet," by JianZhi Robotics, which aims to accelerate the development of household robots through high-quality, real-world data [1][10]. Group 1: Dataset Overview - The dataset consists of over 1 million clips and more than 10,000 hours of embodied data, making it the largest in the industry [1][11]. - It focuses on 10 common household tasks, ensuring that each skill has over 10,000 clips, which provides extensive coverage for individual skills [3][5]. - The dataset is designed to be practical, emphasizing skill depth rather than merely expanding the number of skills [3]. Group 2: Data Quality and Specifications - The dataset features high-quality visuals with a resolution of 1600x1296 and a frame rate of 30 fps, recorded using a large FOV fisheye camera [4][5]. - It achieves centimeter-level trajectory precision, enhanced to sub-centimeter accuracy through high-precision IMU hardware and cloud reconstruction [4][13]. - The dataset includes diverse scenarios and natural human actions, collected from 3,000 real households, addressing the limitations of traditional data collection methods [8][10]. Group 3: Data Production and Automation - JianZhi Robotics has developed a complete data production chain, enabling rapid data collection and processing, accumulating nearly 1 million hours of data in just two months [10][14]. - The Gen DAS Gripper facilitates efficient data collection without the need for extensive site preparation, while the Gen Matrix platform ensures high-precision trajectory alignment and data quality [13][14]. - The Gen ADP automates the data annotation and processing workflow, allowing for continuous and rapid production of high-quality data [14]. Group 4: Importance of Open Data - The article emphasizes the necessity of open data to bridge the data gap, unify technical standards, and lower research and development barriers, ultimately accelerating the transition of embodied intelligence from laboratory settings to practical applications [16]. - The release of the "10Kh RealOmni-Open DataSet" is seen as a significant resource for innovation through data sharing, contributing to a positive feedback loop of data sharing, model optimization, and real-world application [16].

Core Viewpoint - The article emphasizes the importance of high-quality, real-world data for advancing embodied intelligence, highlighting the release of the "10Kh RealOmni-Open DataSet" by JianZhi Robotics as a significant step in this direction [1][4][16]. Data Set Overview - The "10Kh RealOmni-Open DataSet" consists of over 10,000 hours and nearly one million clips of embodied data, making it the largest and most generalized open data set in the industry [1][4]. - This data set focuses on 10 common household tasks, ensuring that each skill has over 10,000 clips, which enhances both the scale and depth of the data [4][5]. Data Quality and Features - The data set boasts a resolution of 1600x1296 pixels at 30 frames per second, with centimeter-level trajectory precision, and aims for millimeter-level accuracy for robotic applications [4][12]. - It includes diverse scenarios and natural human actions, collected from 3,000 real households, addressing the limitations of traditional data collection methods [7][9]. Data Collection Methodology - JianZhi Robotics employs a comprehensive data production chain, utilizing the Gen DAS Gripper for efficient data collection without the need for extensive site preparation [11][13]. - The data collection process is automated, allowing for rapid uploading and processing, with a current accumulation of nearly one million hours of data and a daily growth rate of approximately 10,000 hours [13][14]. Open Source Strategy - The open-sourcing of this data set is intended to bridge data gaps, unify technical standards, and lower research and development barriers, ultimately accelerating the transition of embodied intelligence from laboratory settings to practical applications [16]. - The initiative aims to create a positive feedback loop of data sharing, model optimization, scene implementation, and data reinforcement [16].