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Bayesian Inference on LSTM-based Object Recognition from Tactile and Kinesthetic Information

Pastor, Francisco and García-Gonzalez, Jorge and Gandarias, Juan M. and Medina, Daniel and Closas, Pau and García-Cerezo, Alfonso and Gómez-de-Gabriel, Jesús (2020) Bayesian Inference on LSTM-based Object Recognition from Tactile and Kinesthetic Information. IEEE Robotics and Automation Letters. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/lra.2020.3038377. ISSN 2377-3766.

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Recent advances in the field of intelligent robotic manipulation pursue providing robotic hands with touch sensitivity. Haptic perception encompasses the sensing modalities encountered in the sense of touch (e.g., tactile and kinesthetic sensations). This letter focuses on multimodal object recognition and proposes a Bayesian methodology for the optimal fusion of tactile- and kinesthetic-based classification results. The procedure is as follows: a three-finger actuated gripper with an integrated high-resolution tactile sensor performs squeeze-and-release Exploratory Procedures (EPs). The tactile images and kinesthetic information acquired using angular sensors on the finger joints constitute the time-series datasets of interest. Each temporal dataset is fed to a Long Short-term Memory (LSTM) Neural Network, which is trained to classify in-hand objects. The LSTMs provide an estimation of the posterior probability of each object given the corresponding measurements, which after fusion allows to estimate the object through maximum a posteriori (MAP). An experiment with 24-classes is carried out to evaluate and compare the performance of the fused, tactile, and kinesthetic perception systems. The results show that the fusion-based algorithm improves capabilities for object-recognition.

Item URL in elib:https://elib.dlr.de/135241/
Document Type:Article
Title:Bayesian Inference on LSTM-based Object Recognition from Tactile and Kinesthetic Information
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Pastor, Franciscofpastor (at) uma.esUNSPECIFIED
García-Gonzalez, Jorgejorgegarcia (at) lcc.uma.esUNSPECIFIED
Gandarias, Juan M.jmgandarias (at) uma.esUNSPECIFIED
Medina, DanielDaniel.AriasMedina (at) dlr.dehttps://orcid.org/0000-0002-1586-3269
Closas, Paupau.closas (at) northeastern.eduhttps://orcid.org/0000-0002-5960-6600
García-Cerezo, Alfonsoajgarcia (at) uma.esUNSPECIFIED
Gómez-de-Gabriel, Jesúsjesus.gomez (at) uma.esUNSPECIFIED
Date:June 2020
Journal or Publication Title:IEEE Robotics and Automation Letters
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1109/lra.2020.3038377
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:Haptic; Object Recognition; Bayesian Inference; Classification
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Communication and Navigation
DLR - Research area:Raumfahrt
DLR - Program:R KN - Kommunikation und Navigation
DLR - Research theme (Project):R - Project Navigation 4.0 (old)
Location: Neustrelitz
Institutes and Institutions:Institute of Communication and Navigation > Nautical Systems
Deposited By: Medina, Daniel
Deposited On:10 Jun 2020 18:44
Last Modified:15 Jul 2021 16:26

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