Zhanguang Zhang

Future visual cues can dramatically enhance navigation performance, even when not accessible during actual deployment.

RoboHarness achieves remarkable improvements in long-horizon planning by seamlessly orchestrating diverse robot policies without retraining, even in uncertain environments.

Overcoming perceptual uncertainty in vision-language navigation is now possible by explicitly modeling geometric, semantic, and appearance uncertainty with a novel Uncertainty-Aware Gaussian Map.

Web-scale video pretraining lets robots handle real-world chaos better than vision-language models trained on curated robotics datasets.