Two-stream network-driven vision-based tactile sensor for object feature extraction and fusion perception
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911209879502848 |
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| author | Huang, Muxing Chen, Zibin Xu, Weiliang Li, Zilan Zhou, Yuanzhi Zhou, Guoyuan Chen, Wenjing Li, Xinming |
| author_facet | Huang, Muxing Chen, Zibin Xu, Weiliang Li, Zilan Zhou, Yuanzhi Zhou, Guoyuan Chen, Wenjing Li, Xinming |
| contents | Tactile perception is crucial for embodied intelligent robots to recognize objects. Vision-based tactile sensors extract object physical attributes multidimensionally using high spatial resolution; however, this process generates abundant redundant information. Furthermore, single-dimensional extraction, lacking effective fusion, fails to fully characterize object attributes. These challenges hinder the improvement of recognition accuracy. To address this issue, this study introduces a two-stream network feature extraction and fusion perception strategy for vision-based tactile systems. This strategy employs a distributed approach to extract internal and external object features. It obtains depth map information through three-dimensional reconstruction while simultaneously acquiring hardness information by measuring contact force data. After extracting features with a convolutional neural network (CNN), weighted fusion is applied to create a more informative and effective feature representation. In standard tests on objects of varying shapes and hardness, the force prediction error is 0.06 N (within a 12 N range). Hardness recognition accuracy reaches 98.0%, and shape recognition accuracy reaches 93.75%. With fusion algorithms, object recognition accuracy in actual grasping scenarios exceeds 98.5%. Focused on object physical attributes perception, this method enhances the artificial tactile system ability to transition from perception to cognition, enabling its use in embodied perception applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_12528 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Two-stream network-driven vision-based tactile sensor for object feature extraction and fusion perception Huang, Muxing Chen, Zibin Xu, Weiliang Li, Zilan Zhou, Yuanzhi Zhou, Guoyuan Chen, Wenjing Li, Xinming Robotics Applied Physics Tactile perception is crucial for embodied intelligent robots to recognize objects. Vision-based tactile sensors extract object physical attributes multidimensionally using high spatial resolution; however, this process generates abundant redundant information. Furthermore, single-dimensional extraction, lacking effective fusion, fails to fully characterize object attributes. These challenges hinder the improvement of recognition accuracy. To address this issue, this study introduces a two-stream network feature extraction and fusion perception strategy for vision-based tactile systems. This strategy employs a distributed approach to extract internal and external object features. It obtains depth map information through three-dimensional reconstruction while simultaneously acquiring hardness information by measuring contact force data. After extracting features with a convolutional neural network (CNN), weighted fusion is applied to create a more informative and effective feature representation. In standard tests on objects of varying shapes and hardness, the force prediction error is 0.06 N (within a 12 N range). Hardness recognition accuracy reaches 98.0%, and shape recognition accuracy reaches 93.75%. With fusion algorithms, object recognition accuracy in actual grasping scenarios exceeds 98.5%. Focused on object physical attributes perception, this method enhances the artificial tactile system ability to transition from perception to cognition, enabling its use in embodied perception applications. |
| title | Two-stream network-driven vision-based tactile sensor for object feature extraction and fusion perception |
| topic | Robotics Applied Physics |
| url | https://arxiv.org/abs/2510.12528 |