Investigating Active Sampling for Hardness Classification with Vision-Based Tactile Sensors
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910953668345856 |
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| author | Chen, Junyi Kshirsagar, Alap Heller, Frederik Andreu, Mario Gómez Belousov, Boris Schneider, Tim Lin, Lisa P. Y. Doerschner, Katja Drewing, Knut Peters, Jan |
| author_facet | Chen, Junyi Kshirsagar, Alap Heller, Frederik Andreu, Mario Gómez Belousov, Boris Schneider, Tim Lin, Lisa P. Y. Doerschner, Katja Drewing, Knut Peters, Jan |
| contents | One of the most important object properties that humans and robots perceive through touch is hardness. This paper investigates information-theoretic active sampling strategies for sample-efficient hardness classification with vision-based tactile sensors. We evaluate three probabilistic classifier models and two model-uncertainty-based sampling strategies on a robotic setup as well as on a previously published dataset of samples collected by human testers. Our findings indicate that the active sampling approaches, driven by uncertainty metrics, surpass a random sampling baseline in terms of accuracy and stability. Additionally, while in our human study, the participants achieve an average accuracy of 48.00%, our best approach achieves an average accuracy of 88.78% on the same set of objects, demonstrating the effectiveness of vision-based tactile sensors for object hardness classification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_13231 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Investigating Active Sampling for Hardness Classification with Vision-Based Tactile Sensors Chen, Junyi Kshirsagar, Alap Heller, Frederik Andreu, Mario Gómez Belousov, Boris Schneider, Tim Lin, Lisa P. Y. Doerschner, Katja Drewing, Knut Peters, Jan Robotics Machine Learning One of the most important object properties that humans and robots perceive through touch is hardness. This paper investigates information-theoretic active sampling strategies for sample-efficient hardness classification with vision-based tactile sensors. We evaluate three probabilistic classifier models and two model-uncertainty-based sampling strategies on a robotic setup as well as on a previously published dataset of samples collected by human testers. Our findings indicate that the active sampling approaches, driven by uncertainty metrics, surpass a random sampling baseline in terms of accuracy and stability. Additionally, while in our human study, the participants achieve an average accuracy of 48.00%, our best approach achieves an average accuracy of 88.78% on the same set of objects, demonstrating the effectiveness of vision-based tactile sensors for object hardness classification. |
| title | Investigating Active Sampling for Hardness Classification with Vision-Based Tactile Sensors |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2505.13231 |