Tactile Recognition of Both Shapes and Materials with Automatic Feature Optimization-Enabled Meta Learning
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arXiv
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| Format: | Preprint |
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2026
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| author | Zhao, Hongliang Yang, Wenhui Chen, Yang Wang, Zhuorui Liu, Baiheng Qin, Longhui |
| author_facet | Zhao, Hongliang Yang, Wenhui Chen, Yang Wang, Zhuorui Liu, Baiheng Qin, Longhui |
| contents | Tactile perception is indispensable for robots to implement various manipulations dexterously, especially in contact-rich scenarios. However, alongside the development of deep learning techniques, it meanwhile suffers from training data scarcity and a time-consuming learning process in practical applications since the collection of a large amount of tactile data is costly and sometimes even impossible. Hence, we propose an automatic feature optimization-enabled prototypical network to realize meta-learning, i.e., AFOP-ML framework. As a ``learn to learn" network, it not only adapts to new unseen classes rapidly with few-shot, but also learns how to determine the optimal feature space automatically. Based on the four-channel signals acquired from a tactile finger, both shapes and materials are recognized. On a 36-category benchmark, it outperforms several existing approaches by attaining an accuracy of 96.08% in 5-way-1-shot scenario, where only 1 example is available for training. It still remains 88.7% in the extreme 36-way-1-shot case. The generalization ability is further validated through three groups of experiment involving unseen shapes, materials and force/speed perturbations. More insights are additionally provided by this work for the interpretation of recognition tasks and improved design of tactile sensors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_08423 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Tactile Recognition of Both Shapes and Materials with Automatic Feature Optimization-Enabled Meta Learning Zhao, Hongliang Yang, Wenhui Chen, Yang Wang, Zhuorui Liu, Baiheng Qin, Longhui Robotics Tactile perception is indispensable for robots to implement various manipulations dexterously, especially in contact-rich scenarios. However, alongside the development of deep learning techniques, it meanwhile suffers from training data scarcity and a time-consuming learning process in practical applications since the collection of a large amount of tactile data is costly and sometimes even impossible. Hence, we propose an automatic feature optimization-enabled prototypical network to realize meta-learning, i.e., AFOP-ML framework. As a ``learn to learn" network, it not only adapts to new unseen classes rapidly with few-shot, but also learns how to determine the optimal feature space automatically. Based on the four-channel signals acquired from a tactile finger, both shapes and materials are recognized. On a 36-category benchmark, it outperforms several existing approaches by attaining an accuracy of 96.08% in 5-way-1-shot scenario, where only 1 example is available for training. It still remains 88.7% in the extreme 36-way-1-shot case. The generalization ability is further validated through three groups of experiment involving unseen shapes, materials and force/speed perturbations. More insights are additionally provided by this work for the interpretation of recognition tasks and improved design of tactile sensors. |
| title | Tactile Recognition of Both Shapes and Materials with Automatic Feature Optimization-Enabled Meta Learning |
| topic | Robotics |
| url | https://arxiv.org/abs/2603.08423 |