Learning From a Steady Hand: A Weakly Supervised Agent for Robot Assistance under Microscopy
The paper introduces a weakly supervised framework for steady-hand robotic manipulation using microscopy. It uses reusable warm-up trajectories to implicitly learn spatial information, enabling calibration-aware, depth-resolved perception without manual labeling or external fiducials. The system establishes an error budget from recorded movements and achieves high precision (49µm lateral) while significantly reducing operator workload by 77.1% compared to manual assistance.