ArticleSensors (Basel, Switzerland)2019
Using 3D Convolutional Neural Networks for Tactile Object Recognition with Robotic Palpation.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Spin-based in-sensor computing magnetic tactile sensor for rapid identification of underwater targets.Microsystems & nanoengineering · 2026Article
- Intelligent Sensing: The Emerging Integration of Machine Learning and Soft Sensors Based on Hydrogels and Ionogels.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Robotics Perception and Control: Key Technologies and Applications.Micromachines · 2024Review
- Hardness-and-Type Recognition of Different Objects Based on a Novel Porous Graphene Flexible Tactile Sensor Array.Micromachines · 2023Article
- MEMS-Based Tactile Sensors: Materials, Processes and Applications in Robotics.Micromachines · 2022Review
- Dataset with Tactile and Kinesthetic Information from a Human Forearm and Its Application to Deep Learning.Sensors (Basel, Switzerland) · 2022Article
- Identifying the Strength Level of Objects' Tactile Attributes Using a Multi-Scale Convolutional Neural Network.Sensors (Basel, Switzerland) · 2022Article
- Human Somatosensory Processing and Artificial Somatosensation.Cyborg and bionic systems (Washington, D.C.) · 2021Review
- Neural Networks Predicting Microbial Fuel Cells Output for Soft Robotics Applications.Frontiers in robotics and AI · 2021Article
- Tactile Sensors for Robotic Applications.Sensors (Basel, Switzerland) · 2020Article
- Artificial Intelligence-Based Optimal Grasping Control.Sensors (Basel, Switzerland) · 2020Article
- A Novel Bilinear Feature and Multi-Layer Fused Convolutional Neural Network for Tactile Shape Recognition.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
4 authors.
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Abstract
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.
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