Learning From a Steady Hand: A Weakly Supervised Agent for Robot Assistance under Microscopy

Abstract: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 frame-by-frame manual depth annotation. By explicitly characterizing residuals from observation and calibration models, the system establishes a task-space error budget from recorded warm-ups. Under the evaluated laboratory conditions, the recorded trajectories yielded an empirical lateral error level of approximately 49{\mu}m and a conservative axial error estimate of approximately 291 {\mu}m 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. Together, these results demonstrate successful closed-loop micromanipulation and reduced subjective workload under the evaluated laboratory conditions, without external fiducials or frame-by-frame manual depth annotation, supporting the proposed approach as a framework for microscope-guided robotic assistance.

Robotics (cs.RO)

arXiv:2601.20776 [cs.RO]

(or arXiv:2601.20776v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2601.20776 Focus to learn more arXiv-issued DOI via DataCite

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