Stable In-Grasp Manipulation with a Low-Cost Robot Hand by Using 3-Axis Tactile Sensors with a CNN
| Autor: | Tetsuya Ogata, Tomoki Isobe, Shun Ogasa, Tito Pradhono Tomo, Shigeki Sugano, Alexander Schmitz, Satoshi Funabashi |
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| Rok vydání: | 2020 |
| Předmět: |
Contact states
0303 health sciences 0209 industrial biotechnology 030306 microbiology business.industry Computer science GRASP Process (computing) Robot hand 02 engineering and technology 03 medical and health sciences 020901 industrial engineering & automation Intelligent sensor Computer vision Artificial intelligence business Tactile sensor |
| Zdroj: | IROS |
| DOI: | 10.1109/iros45743.2020.9341362 |
| Popis: | The use of tactile information is one of the most important factors for achieving stable in-grasp manipulation. Especially with low-cost robotic hands that provide low-precision control, robust in-grasp manipulation is challenging. Abundant tactile information could provide the required feed-back to achieve reliable in-grasp manipulation also in such cases. In this research, soft distributed 3-axis skin sensors ("uSkin") and 6-axis F/T (force/torque) sensors were mounted on each fingertip of an Allegro Hand to provide rich tactile information. These sensors yielded 78 measurements for each fingertip (72 measurements from the uSkin and 6 measurements from the 6-axis F/T sensor). However, such high-dimensional tactile information can be difficult to process because of the complex contact states between the grasped object and the fingertips. Therefore, a convolutional neural network (CNN) was employed to process the tactile information. In this paper, we explored the importance of the different sensors for achieving in-grasp manipulation. Successful in-grasp manipulation with untrained daily objects was achieved when both 3-axis uSkin and 6-axis F/T information was provided and when the information was processed using a CNN. |
| Databáze: | OpenAIRE |
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