IntelliMan_WP5_Grasping, Manipulation and Arm-Hand Coordination_T5.1_Data fusion and sensing technology_IEEESENSORS2026_Data

The dataset consists of tactile signals acquired from two flexible PVDF sensor arrays (32 sensors total) integrated into soft sensing caps mounted on a TIAGo robotic gripper while grasping daily-life objects. Six objects (glass bottle, TV remote controller, plastic bottle, strawberry, rigid apple, and semi-soft apple) were each class consists of 50 trials, resulting in 300 trials. The dataset was segmented into short temporal windows and used to recognize objects at the early stage of grasping using machine-learning and deep-learning models, enabling real-time tactile-based object classification during the initial contact phase of grasping. Moreover, the dataset was used to perform a systematic evaluation to assess the effect number of sensors needed for accurate recognition.