INTELLIMAN. WP4. Adaptive shared autonomy. T4_2. Advanced human-robot interaction modalities. Piezo Skin. v0

The dataset is related to tactile sensing for robotic manipulation, with a specific focus on contact event classification and continuous contact force regression using a Piezoelectric Tactile Skin (PTS). It includes tactile signal data acquired from an array of 8 piezoelectric sensors encapsulated in a compliant skin and mounted on both a human fingertip and the fingertip of an anthropomorphic robotic hand. The dataset supports the analysis and comparison of different feature representations extracted from tactile signals—raw tactile signals, Short-Time Fourier Transform (STFT) features, and Discrete Wavelet Transform (DWT) marginals—when used with machine learning models (Support Vector Machines for classification and Neural Networks for regression).
The dataset was acquired from experimental trials designed to characterize the PTS response under structured pressure and sliding tasks. Normal forces applied by the fingertips were measured using a multi-axis force/torque sensor equipped with a metal plate, and used as ground-truth for labeling and regression targets. The dataset is intended for evaluating performance metrics such as classification accuracy and regression RMSE, enabling the assessment of how time–frequency features improve tactile interpretation compared to raw signals.
The data were collected within a dedicated tactile acquisition setup integrating: (i) the piezoelectric tactile system connected to an embedded electronics unit, (ii) a force/torque sensing unit used as reference, and (iii) in the robotic scenario, an anthropomorphic AR10 humanoid robot hand. Data synchronization and recording were handled in a ROS-based pipeline with custom scripts. The dataset supports the analysis of tactile-driven recognition of contact states, force levels, and sliding phenomena, as well as continuous force prediction from piezoelectric tactile signals.
The dataset is associated with the following publication:
N. Alati, D. Bargellini, A. Pasquali, Y. Abbass, M. Valle, G. Palli, and R. Meattini,
“Leveraging Time-Frequency Features For Contact Classification And Regression With A Piezoelectric Tactile Skin For Robotic Fingertips,”
Proceedings of the 2025 55th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), 2025. https://doi.org/10.1109/DSN-W65791.2025.00041