RT Journal Article
T1 Using 3D Convolutional Neural Networks for Tactile Object Recognition with Robotic Palpation.
A1 Pastor-Martín, Francisco
A1 Gandarias Palacios, Juan Manuel
A1 García-Cerezo, Alfonso José
A1 Gómez-de-Gabriel, Jesús Manuel
K1 Detectores
K1 Robótica
K1 Aprendizaje automático (Inteligencia artificial)
K1 Redes neuronales (Informática)
AB In this paper, a novel method of active tactile perception based on 3D neural networks and a high-resolution tactile sensor installed on a robot gripper is presented. A haptic exploratory procedure based on robotic palpation is performed to get pressure images at different grasping forces that provide information not only about the external shape of the object, but also about its internal features. The gripper consists of two underactuated fingers with a tactile sensor array in the thumb. A new representation of tactile information as 3D tactile tensors is described. During a squeeze-and-release process, the pressure images read from the tactile sensor are concatenated forming a tensor that contains information about the variation of pressure matrices along with the grasping forces. These tensors are used to feed a 3D Convolutional Neural Network (3D CNN) called 3D TactNet, which is able to classify the grasped object through active interaction. Results show that 3D CNN performs better, and provide better recognition rates with a lower number of training data.
PB MDPI
YR 2019
FD 2019-12-05
LK https://hdl.handle.net/10630/34034
UL https://hdl.handle.net/10630/34034
LA eng
NO Pastor, F.; Gandarias, J.M.; García-Cerezo, A.J.; Gómez-de-Gabriel, J.M. Using 3D Convolutional Neural Networks for Tactile Object Recognition with Robotic Palpation. Sensors 2019, 19, 5356. https://doi.org/10.3390/s19245356
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026