开源！国内规模最大的全尺寸人形机器人真机数据集！哪里值得关注

Core Viewpoint - The LET dataset, the world's first full-size humanoid robot real-world operation dataset, addresses the critical shortage of high-quality, large-scale, standardized real-world operational data, which has been a significant barrier to the advancement of humanoid robots and embodied intelligence [1][5]. Group 1: Data Scarcity in Humanoid Robotics - Humanoid robot data is scarce due to the dual barriers of technology and cost, with the "Scaling Law" indicating that model performance improves significantly with increased data volume, model size, and computational power [3][4]. - Real-world data collection is costly, with traditional methods yielding only three to four valid data points per hour at a cost of nearly twenty yuan per data point, leading to annual costs approaching three hundred thousand yuan for manual collection [4]. Group 2: LET Dataset Release - The LET dataset, developed by Leju Intelligent and other institutions, is the largest open-source dataset of its kind in China, featuring over 60,000 minutes of real machine data collected from the "Kua Fu" humanoid robot [5][7]. - The dataset incorporates innovative technologies to ensure high data quality, achieving over 90% consistency and controlling timestamp errors within ten milliseconds, which enhances the robustness and transferability of models trained on this data [7]. Group 3: Comprehensive Scene Coverage - The LET dataset covers three core areas: industrial, commercial retail, and daily life, detailing six real-world operational scenarios and encompassing 31 key tasks and 117 atomic skills [8]. - This extensive coverage allows developers to quickly adapt to vertical industry needs, facilitating the transition from technology validation to large-scale application of embodied intelligence [8]. Group 4: Tools and Future Implications - To lower the usage threshold and accelerate technology transfer, the LET dataset provides a comprehensive toolchain for data conversion, model training, simulation testing, and real machine deployment, enabling developers to achieve "plug-and-play" functionality [10]. - The release of the LET dataset not only fills the gap in high-quality real machine data but also supports the scaling law for humanoid robots, fostering a virtuous cycle of data sharing, technological iteration, and application optimization [11].