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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.20776 |
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| _version_ | 1866918311823933440 |
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| author | Tian, Huanyu Huber, Martin Zeng, Lingyun Han, Zhe Bennett, Wayne Silvestri, Giuseppe Mendizabal-Ruiz, Gerardo Vercauteren, Tom Chavez-Badiola, Alejandro Bergeles, Christos |
| author_facet | Tian, Huanyu Huber, Martin Zeng, Lingyun Han, Zhe Bennett, Wayne Silvestri, Giuseppe Mendizabal-Ruiz, Gerardo Vercauteren, Tom Chavez-Badiola, Alejandro Bergeles, Christos |
| contents | This paper rethinks steady-hand robotic manipulation by using a weakly supervised framework that fuses calibration-aware perception with admittance control. Unlike conventional automation that relies on labor-intensive 2D labeling, our framework leverages reusable warm-up trajectories to extract implicit spatial information, thereby achieving calibration-aware, depth-resolved perception without the need for external fiducials or manual depth annotation. By explicitly characterizing residuals from observation and calibration models, the system establishes a task-space error budget from recorded warm-ups. The uncertainty budget yields a lateral closed-loop accuracy of approx. 49 micrometers at 95% confidence (worst-case testing subset) and a depth accuracy of <= 291 micrometers at 95% confidence bound during large in-plane moves. In a within-subject user study (N=8), the learned agent reduces overall NASA-TLX workload by 77.1% relative to the simple steady-hand assistance baseline. These results demonstrate that the weakly supervised agent improves the reliability of microscope-guided biomedical micromanipulation without introducing complex setup requirements, offering a practical framework for microscope-guided intervention. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_20776 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning From a Steady Hand: A Weakly Supervised Agent for Robot Assistance under Microscopy Tian, Huanyu Huber, Martin Zeng, Lingyun Han, Zhe Bennett, Wayne Silvestri, Giuseppe Mendizabal-Ruiz, Gerardo Vercauteren, Tom Chavez-Badiola, Alejandro Bergeles, Christos Robotics This paper rethinks steady-hand robotic manipulation by using a weakly supervised framework that fuses calibration-aware perception with admittance control. Unlike conventional automation that relies on labor-intensive 2D labeling, our framework leverages reusable warm-up trajectories to extract implicit spatial information, thereby achieving calibration-aware, depth-resolved perception without the need for external fiducials or manual depth annotation. By explicitly characterizing residuals from observation and calibration models, the system establishes a task-space error budget from recorded warm-ups. The uncertainty budget yields a lateral closed-loop accuracy of approx. 49 micrometers at 95% confidence (worst-case testing subset) and a depth accuracy of <= 291 micrometers at 95% confidence bound during large in-plane moves. In a within-subject user study (N=8), the learned agent reduces overall NASA-TLX workload by 77.1% relative to the simple steady-hand assistance baseline. These results demonstrate that the weakly supervised agent improves the reliability of microscope-guided biomedical micromanipulation without introducing complex setup requirements, offering a practical framework for microscope-guided intervention. |
| title | Learning From a Steady Hand: A Weakly Supervised Agent for Robot Assistance under Microscopy |
| topic | Robotics |
| url | https://arxiv.org/abs/2601.20776 |