High-Precision Transformer-Based Visual Servoing for Humanoid Robots in Aligning Tiny Objects
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917181571203072 |
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| author | Xue, Jialong Gao, Wei Wang, Yu Ji, Chao Zhao, Dongdong Yan, Shi Zhang, Shiwu |
| author_facet | Xue, Jialong Gao, Wei Wang, Yu Ji, Chao Zhao, Dongdong Yan, Shi Zhang, Shiwu |
| contents | High-precision tiny object alignment remains a common and critical challenge for humanoid robots in real-world. To address this problem, this paper proposes a vision-based framework for precisely estimating and controlling the relative position between a handheld tool and a target object for humanoid robots, e.g., a screwdriver tip and a screw head slot. By fusing images from the head and torso cameras on a robot with its head joint angles, the proposed Transformer-based visual servoing method can correct the handheld tool's positional errors effectively, especially at a close distance. Experiments on M4-M8 screws demonstrate an average convergence error of 0.8-1.3 mm and a success rate of 93\%-100\%. Through comparative analysis, the results validate that this capability of high-precision tiny object alignment is enabled by the Distance Estimation Transformer architecture and the Multi-Perception-Head mechanism proposed in this paper. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_04862 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | High-Precision Transformer-Based Visual Servoing for Humanoid Robots in Aligning Tiny Objects Xue, Jialong Gao, Wei Wang, Yu Ji, Chao Zhao, Dongdong Yan, Shi Zhang, Shiwu Computer Vision and Pattern Recognition Robotics High-precision tiny object alignment remains a common and critical challenge for humanoid robots in real-world. To address this problem, this paper proposes a vision-based framework for precisely estimating and controlling the relative position between a handheld tool and a target object for humanoid robots, e.g., a screwdriver tip and a screw head slot. By fusing images from the head and torso cameras on a robot with its head joint angles, the proposed Transformer-based visual servoing method can correct the handheld tool's positional errors effectively, especially at a close distance. Experiments on M4-M8 screws demonstrate an average convergence error of 0.8-1.3 mm and a success rate of 93\%-100\%. Through comparative analysis, the results validate that this capability of high-precision tiny object alignment is enabled by the Distance Estimation Transformer architecture and the Multi-Perception-Head mechanism proposed in this paper. |
| title | High-Precision Transformer-Based Visual Servoing for Humanoid Robots in Aligning Tiny Objects |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2503.04862 |