Shape-independent hardness estimation using deep learning and a GelSight tactile sensor
| العنوان: | Shape-independent hardness estimation using deep learning and a GelSight tactile sensor |
|---|---|
| المؤلفون: | Andrew Owens, Edward H. Adelson, Chenzhuo Zhu, Wenzhen Yuan, Mandayam A. Srinivasan |
| المساهمون: | 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, Yuan, Wenzhen, Zhu, Chenzhuo, Owens, Andrew Hale, Srinivasan, Mandayam A, Adelson, Edward H |
| المصدر: | ICRA MIT Web Domain |
| بيانات النشر: | IEEE, 2017. |
| سنة النشر: | 2017 |
| مصطلحات موضوعية: | FOS: Computer and information sciences, 0209 industrial biotechnology, business.industry, Computer science, Deep learning, 010401 analytical chemistry, 02 engineering and technology, Slip (materials science), 01 natural sciences, 0104 chemical sciences, Contact force, Computer Science - Robotics, 020901 industrial engineering & automation, Robot, Computer vision, Artificial intelligence, business, Robotics (cs.RO), Tactile sensor |
| الوصف: | 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. |
| وصف الملف: | application/pdf |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fc05285b1685eeb3db0b3407d115d60f https://doi.org/10.1109/icra.2017.7989116 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....fc05285b1685eeb3db0b3407d115d60f |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |