Object Recognition Through Active Sensing Using a Multi-Fingered Robot Hand with 3D Tactile Sensors
| Autor: | Tito Pradhono Torno, Shu Morikuni, Shun Ogasa, Andreas Geier, Sophon Somlor, Shigeki Sugano, Satoshi Funabashi, Alexander Schmitz |
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| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Normal force Computer science business.industry 010401 analytical chemistry Feed forward Cognitive neuroscience of visual object recognition Active sensing 02 engineering and technology 01 natural sciences Convolutional neural network 0104 chemical sciences 020901 industrial engineering & automation Computer vision Artificial intelligence business Tactile sensor Test data |
| Zdroj: | IROS |
| DOI: | 10.1109/iros.2018.8594159 |
| Popis: | This paper investigates tactile object recognition with relatively densely distributed force vector measurements and evaluates what kind of tactile information is beneficial for object recognition. The uSkin tactile sensors are embedded in an Allegro Hand, and provide 240 triaxial force vector measurements in total in all fingers. Active object sensing is used to gather time-series training and testing data. A simple feedforward, a recurrent, and a convolutional neural network are used for recognizing objects. Evaluations with different number of employed measurements, static vs. time series data and force vector vs. only normal force vector measurements show that the high-dimensional information provided by the sensors is indeed beneficial. An object recognition rate of up to 95% for 20 objects was achieved. |
| Databáze: | OpenAIRE |
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