2019 Conference on Cognitive Computational Neuroscience

13-16 September 2019    Berlin, Germany

Technical Program

Paper Detail

Paper: PS-1B.21
Session: Poster Session 1B
Location: H Fläche 1.OG
Session Time: Saturday, September 14, 16:30 - 19:30
Presentation Time:Saturday, September 14, 16:30 - 19:30
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: Artificial haptic recognition through human manipulation of objects
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
DOI: https://doi.org/10.32470/CCN.2019.1240-0
Authors: David Miralles, Carlota Parés, Guillem Garrofé, Alberto Soto, Albert Llauradó, Gerard Serra, Àlex Falcó, La Salle - Univesitat Ramon Llull, Spain; Hans Op de Beeck, Haemy Lee Masson, KU Leuven, Belgium
Abstract: Object recognition has been extensively explored in the computer vision literature, and over the last few years the results in this field have sometimes even surpassed human performance. One of the main reasons for this success is the growing number of images available to generate training datasets for machine learning. In comparison to computer vision, haptic approaches to object recognition have received relatively little attention, probably due to the inadequacy of available sensors to gather the huge amount of data needed to train the modern machine learning algorithms. Consequently, the performance of machine haptic recognition of objects is still far from being comparable with humans. In this paper, we first present a new sensor system capable of capturing part of the information that humans produce during the haptic manipulation of objects and an artificial haptic intelligence that classifies shapes from the dataset created by the sensor system. Secondly, we compare the haptic object recognition performance between humans and a machine. The current study sheds new light upon the novel approach used to capture human haptic exploration and provides evidence that artificial haptic intelligence outperforms human haptic recognition abilities.