A multimodal tactile dataset for texture classification on uneven surfaces using a 6-DoF robotic arm

This article presents a comprehensive tactile dataset designed to facilitate the perception and classification of textures on uneven surfaces. The data was collected using a 6-DoF Lite6 robotic manipulator equipped with a BioIn-Tacto multimodal sensing module. The data acquisition process consists of two main phases. First, an initial probing procedure is conducted to acquire 12 reference points on the curved surface. During this phase, the sensor's Inertial Measurement Unit (IMU) orientation is recorded precisely when the embedded barometer registers contact pressure, accurately capturing the surface normal. Subsequently, the robotic arm performs dynamic exploration by sliding the sensor along the generated trajectory, maintaining consistent contact with the surface. The dataset comprises multi-modal measurements from 12 distinct materials (including various fabrics, wood, leather, and plastics) mounted on curved geometries, with 25 exploratory episodes recorded per texture. The hardware captures nine-degrees-of-freedom Magnetic, Angular Rate, and Gravity (MARG) data alongside deep-pressure readings from a barometer encased in polyurethane rubber. This dataset has substantial reuse potential for researchers developing machine learning models and AI-driven sensorimotor policies for tactile texture classification in realistic environments. Furthermore, as it complements a previous dataset collected with a 4-DoF robot on identical surfaces, it can be used for cross-embodiment research, enabling the community to benchmark sensor performance and evaluate the impact of kinematic constraints on robotic tactile perception.