XinZhi Embodied and Fudan University have jointly released three technical reports addressing tactile sensing in embodied intelligence, publishing 30,000 hours of tactile interaction data collected from real robot operations. The data is designed to train models to understand physical properties such as texture, hardness, temperature, and friction through touch. The three reports cover tactile sensor design and calibration, large-scale tactile data collection methodology, and tactile-aware manipulation model training. The sensor approach combines capacitive, piezoelectric, and resistive sensing to capture static pressure distribution and dynamic vibration signals. Data collection employed multiple robot platforms performing grasping, in-hand manipulation, and surface exploration tasks across thousands of objects. By open-sourcing 30,000 hours of structured tactile interaction data, the collaboration aims to accelerate research into touch-enabled manipulation, enabling robots to handle objects and materials in real-world environments rather than being limited to visual-only operation in controlled settings. The work aligns with the broader trend toward multi-modal perception in embodied intelligence.