Yanda Huang

Papers

Total Citations

H-Index

About

Yanda Huang is a leading researcher in human-robot interaction and crowd-augmented autonomy, with a focus on enabling robots to understand and operate in complex, unstructured environments. His most-cited work, “EURECA: Enhanced Understanding of Real Environments via Crowd Assistance” (2018, 15 citations), introduces a novel framework that leverages real-time human crowd input to help robots interpret unfamiliar objects and scenes—a critical challenge for assistive robotics. By combining computer vision with on-demand crowd intelligence, Huang’s research bridges the gap between autonomous perception and real-world variability, directly improving robots’ ability to assist people with disabilities and perform everyday tasks in dynamic indoor settings. His contributions have been recognized for their practical impact on accessible robotics, and his work continues to influence the design of systems that rely on human-machine collaboration. With a growing citation record and a focus on scalable, human-in-the-loop solutions, Huang is shaping how robots learn from and adapt to the environments they are meant to serve.