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: | , , , , |
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| Format: | Article |
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Institute of Electrical and Electronics Engineers (IEEE)
2017
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| Online Access: | http://hdl.handle.net/1721.1/111980 https://orcid.org/0000-0001-8014-356X https://orcid.org/0000-0001-9020-9593 https://orcid.org/0000-0003-1347-6502 https://orcid.org/0000-0003-2222-6775 |