Dapeng Zhang

Microscopic robots can now execute complex tasks from natural language instructions with unprecedented accuracy, achieving 6.30-pixel RMSE using zero new labels and outperforming specialized models by over 50%.

A hierarchical framework that couples high-level multimodal reasoning with low-level function-constrained execution for deployable precision medical robotics, and introduces a scalable multi-agent pipeline to generate skill-oriented chain-of-thought data for structured training.

HALO-WA boosts robotic manipulation success rates from 26.4% to 87.1% by effectively adapting to real-world errors in just over an hour of training.