Microscopic robots follow language instructions with improved accuracy
From Language to Motion: Task-Conditioned Focal-Stack Trajectory Integration for Microscopic Robots
Robotics
Summary
Microscopic robots need to move precisely based on changing instructions, parts, and focus settings. This paper introduces a way to convert language commands into physical movements by combining vision and geometry techniques. The authors show their method improves accuracy in following paths and reduces the need for extensive training data. Their experiments demonstrate better coverage of target areas and more reliable task execution.
What this means in practice
- •For microrobotics engineers: Enable microscopic robots to perform precise movements based on flexible, language-based task descriptions without retraining on new parts.
- •For manufacturing automation teams: Improve accuracy in automated dispensing and manipulation tasks by integrating visual focus data to better guide microscopic robotic paths.
Authors
Junjie Xie, Chuxuan He, Junkai Huang, Heng Zhang, Angen Ye, Yujia Song, Yuqing Li, Pengsong Zhang, Dapeng Zhang
Abstract
Microscopic robots require accurate task geometry despite changes in language, parts, and focus. We present a semantic-to-physical framework that maps instructions to constrained geometric operators, reuses frozen open-vocabulary perception, and integrates locally reliable focal-plane trajectories by confidence weighting and dynamic programming. Calibrated multi-view geometry 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 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.