Linghao Chen

University of California San Diego, Zhejiang University

Papers

Total Citations

H-Index

About

Linghao Chen is a robotics researcher whose work focuses on advancing perception and calibration for autonomous manipulation. His primary research areas include hand-eye calibration, interactive 3D object perception, and robot autonomy. Chen’s major contributions center on automating traditionally labor-intensive calibration processes. His most cited work, “EasyHeC” (2023, 17 citations), introduces a novel framework that leverages differentiable rendering and space exploration to achieve accurate, markerless hand-eye calibration—eliminating the need for specialized joint poses or calibration targets. He extended this line of research with “EasyHeC++” (2024, 4 citations), which incorporates pretrained image models for fully automatic calibration. In a different direction, Chen’s “Perceiving Unseen 3D Objects by Poking the Objects” (2023, 7 citations) presents an interactive approach where robots physically poke unknown objects to discover and reconstruct their 3D geometry without requiring prior models or extensive annotated data. This work demonstrates his interest in active perception for unstructured environments. With a growing citation impact, Chen’s research is making calibration more accessible and enabling robots to perceive and interact with novel objects—key steps toward more adaptable robotic systems.