Shape-independent hardness estimation using deep learning and a GelSight tactile sensor
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1704.03955.pdf
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Author(s)
Yuan, Wenzhen
Zhu, Chenzhuo
Owens, Andrew Hale
Srinivasan, Mandayam A
Adelson, Edward H
Date Issued
July 2017
Journal
2017 IEEE International Conference on Robotics and Automation (ICRA)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Yuan, Wenzhen et al. “Shape-Independent Hardness Estimation Using Deep Learning and a GelSight Tactile Sensor.” 2017 IEEE International Conference on Robotics and Automation (ICRA) May 29 - June 3 2017, Singapore, Institute of Electrical and Electronics Engineers (IEEE), July 2017 © 2017 Institute of Electrical and Electronics Engineers (IEEE)
Version
Author's final manuscript
Abstract
Hardness is among the most important attributes of an object that humans learn about through touch. However, approaches for robots to estimate hardness are limited, due to the lack of information provided by current tactile sensors. In this work, we address these limitations by introducing a novel method for hardness estimation, based on the GelSight tactile sensor, and the method does not require accurate control of contact conditions or the shape of objects. A GelSight has a soft contact interface, and provides high resolution tactile images of contact geometry, as well as contact force and slip conditions. In this paper, we try to use the sensor to measure hardness of objects with multiple shapes, under a loosely controlled contact condition. The contact is made manually or by a robot hand, while the force and trajectory are unknown and uneven. We analyze the data using a deep constitutional (and recurrent) neural network. Experiments show that the neural net model can estimate the hardness of objects with different shapes and hardness ranging from 8 to 87 in Shore 00 scale.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Laboratory for Human and Machine Haptics
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/ICRA.2017.7989116
