Core Viewpoint - Lumos Robotics has launched a revolutionary product, the FastUMI Pro, which addresses the critical bottleneck in acquiring high-quality, large-scale, multi-modal data essential for the advancement of embodied intelligence [1][21]. Group 1: Product Features - FastUMI Pro features a lightweight design with a total weight of only 600g and a load capacity of up to 2kg, enhancing portability and enabling data collection in any scenario [3][10]. - The system boasts a global leading pure visual positioning technology with an accuracy of up to 3mm, eliminating reliance on heavy laser and fixed base stations [6][17]. - It supports multi-modal data collection, integrating pressure-sensitive and tactile sensors, which allows for comprehensive data capture during operations [8][19]. - FastUMI Pro can quickly adapt to various robots without the need for body replacement, showcasing high compatibility [9]. - The system significantly improves data collection efficiency by three times and reduces costs to one-fifth of traditional solutions, addressing the industry's high-cost and high-barrier challenges [11][21]. Group 2: Ecosystem and Integration - FastUMI Pro is not just a hardware device but a complete ecosystem that provides an end-to-end solution from hardware design to model training, facilitating a seamless data pipeline for researchers [13][21]. - The system includes a real-time pre-processing architecture that allows users to verify data validity during collection, thus preventing ineffective data acquisition [19][20]. - It features a four-eye vision system that enhances environmental feature capture, ensuring stable operation even in challenging lighting and occlusion conditions [19]. Group 3: Market Impact - The introduction of FastUMI Pro marks a significant step for Lumos Robotics in the core infrastructure of embodied intelligence, aiming to lower the barriers for acquiring high-quality data [21]. - The product is positioned to accelerate the scaling process of intelligent systems by enabling global research teams to obtain diverse data at lower costs and higher efficiency [21].