From Language to Motion: Task-Conditioned Focal-Stack Trajectory Integration for Microscopic Robots
From Language to Motion: Task-Conditioned Focal-Stack Trajectory Integration for Microscopic Robots
This research presents a framework that maps language instructions to geometric operators for microscopic robots, integrating focal-plane trajectories with confidence weighting and dynamic programming. The method improves trajectory accuracy and efficiency, achieving RMSE reductions of up to 56.4% and P95 error reductions of 59.5% compared to image-first multi-focus fusion. The approach also enhances target-region coverage and provides a measurable physical trace of the robot's actions.
Source: arXiv Robotics — research abstracts · Read original article ↗
Loading article text…
What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
RMSE
6.3–6.59
time
15
time
72–165 minutes
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
ometry connects 2-D paths to physical execution. Prompt, unseen-part, and geometry reconfiguration tests yield 6.30-6.59-pixel RMSE. Relative to part-specific U-Net training with 20-100 labels, the proposed zero-new-label configuration takes 15 rather than 72-165 min. Across nine part-illumination conditions, trajectory-space integration reduces RMSE from 14.41 to 6.28 pixels (56.4%) and P95 error from 20.07 to 8.13
Open source S3
pixels (59.5%) compared with image-first multi-focus fusion. An ablation isolates the roles of confidence and path-wise selection. In representative robot experiments, target-region coverage improves from 83.5% to 92.9%. Dispensing provides a measurable physical trace, not a task-specific limitation of the method.
Open source S4
Source：arXiv Robotics — research abstracts · arxiv.org