Superhuman Performance in Tactile Material Classification and Differentiation with a Flexible Pressure-Sensitive Skin
| Autor: | Andreea Tulbure, Berthold Bauml |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
robotics
0209 industrial biotechnology Network architecture business.industry Computer science Deep learning 010401 analytical chemistry SIGNAL (programming language) deep learning 02 engineering and technology 01 natural sciences tactile sensing 0104 chemical sciences Task (project management) Material classification 020901 industrial engineering & automation Pressure sensitive Robot Computer vision Artificial intelligence Architecture business |
| Zdroj: | Humanoids |
| DOI: | 10.1109/humanoids.2018.8624987 |
| Popis: | In this paper, we show that a robot equipped with a flexible and commercially available tactile skin can exceed human performance in the challenging tasks of material classification, i.e., uniquely identifying a given material by touch alone, and of material differentiation, i.e., deciding if the materials in a given pair of materials are the same or different. For processing the high dimensional spatio-temporal tactile signal, we use a new tactile deep learning network architecture TactNet-II which is based on TactNet [1] and is significantly extended with recently described architectural enhancements and training methods. TactN et- Iireaches an accuracy for the material classification task as high as 95.0 %. For the material differentiation a new Siamese network based architecture is presented which reaches an accuracy as high as 95.4 %. All the results have been achieved on a new challenging dataset of 36 everyday household materials. In a thorough human performance experiment with 15 subjects we show that the human performance is significantly lower than the robot's performance for both tactile tasks. |
| Databáze: | OpenAIRE |
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