Debing Zhang

Institute of Microelectronics, Tsinghua University

Papers

Total Citations

H-Index

About

Debing Zhang is a robotics researcher whose work spans computer vision, reinforcement learning, and surgical robotics. His most cited paper, "High-Speed Tiny Tennis Ball Detection Based on Deep Convolutional Neural Networks" (2020, 16 citations), tackles the challenging problem of detecting fast-moving, small objects in robot vision—a critical capability for sports robotics. Zhang also developed LORM, a novel reinforcement learning framework for biped gait control (2022, 6 citations), which simplifies the complex dynamics of legged locomotion, enabling robots to adapt to diverse terrains. In the medical domain, he contributed to a registration method for total knee arthroplasty surgical robots (2022, 2 citations), improving accuracy in surgical navigation. His recent work on LP-SLAM (2023, 2 citations) integrates large language models with RGB-D SLAM systems, pushing the boundaries of semantic and textual environment perception for autonomous robots. Zhang’s diverse contributions—from sports ball detection to bipedal control and surgical precision—demonstrate his impact across multiple robotics frontiers, with his research collectively cited over 26 times.