VTFTdataset

This dataset provides visual and tactile recordings collected from a robotic grasping platform to support research on multi-modal perception, robot grasping, and sensor fusion. It integrates visual data with high-resolution tactile signals, enabling studies in perception-driven manipulation and multimodal learning. The dataset was collected to validate the proposed multimodal fusion method described in our paper. It is publicly available to promote reproducibility and facilitate further research in robotic perception and control. 1. Data Acquisition PlatformThe data collection platform consists of:• Robot: UR10 robotic arm (position control mode, 125 Hz)• Hand: Barrett BH8-282 three-fingered hand with capacitive tactile sensors• Camera: Intel RealSense RGB-D camera The UR10 performs smooth, high-precision grasping trajectories, while the Barrett hand provides real-time tactile force distribution from the fingertips and palm. 2. Object SetA total of 15 object categories were selected, covering a wide range of materials and surface characteristics:• Materials: plastic, metal, sponge, wood, fabric, ceramic• Textures: smooth, rough, soft, rigid 3. Visual Data• Sensor: Intel RealSense RGB-D camera• Resolution: 1920 × 1080 (RGB)• Operational Range: 0.1 – 10 m• Calibration: - Camera calibration via chessboard pattern - Hand–eye calibration aligns the camera coordinate frame with the UR10 base frame - Ensures spatial consistency between visual and tactile data 4. Tactile Data• Sensor Type: Capacitive tactile array embedded in fingertips and palm• Resolution: Each finger: 8 × 3 sensor cells• Accuracy: ±0.01 N per cell• Calibration: All tactile sensors were zero-calibrated under no-load conditions to eliminate baseline drift and improve measurement repeatability. For the details of dataset or you wanna use our dataset in your research, feel free to contact with [e-mail] in English/Chinese!