From Language to Motion: Task-Conditioned Focal-Stack Trajectory Integration for Microscopic Robots
The framework maps natural language instructions to geometric operators for microscopic robots, reusing frozen perception and integrating focal-plane trajectories using confidence weighting and dynamic programming. This approach achieves accurate task geometry despite changes in language, parts, and focus. It proves that decoupling semantic requests from metric measurement leads to faster adaptation (15 min vs 165 min) and superior physical validation.