Shape-independent hardness estimation using deep learning and a GelSight tactile sensor
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 m...
Main Authors: | Yuan, Wenzhen (Contributor), Zhu, Chenzhuo (Contributor), Owens, Andrew Hale (Contributor), Srinivasan, Mandayam A (Contributor), Adelson, Edward H (Contributor) |
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Other Authors: | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences (Contributor), Massachusetts Institute of Technology. Department of Mechanical Engineering (Contributor), Massachusetts Institute of Technology. Laboratory for Human and Machine Haptics (Contributor) |
Format: | Article |
Language: | English |
Published: |
Institute of Electrical and Electronics Engineers (IEEE),
2017-10-27T13:55:46Z.
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Subjects: | |
Online Access: | Get fulltext |
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