Caiming Zheng

Papers

Total Citations

H-Index

About

Caiming Zheng is a pioneering researcher at the intersection of humanoid robotics, affective computing, and human-robot interaction. His work addresses fundamental challenges in making robots more energy-efficient, socially aware, and capable of natural collaboration with humans. Zheng’s most influential contribution is the development of optimal parameterization methods for cooperative motion control, enabling two humanoid robots to execute complex, low-energy joint movements—a breakthrough that directly tackles the high power consumption bottleneck in legged robotics. He also introduced the interpretable CASE personality model and a willingness-based framework for task allocation, allowing multi-robot systems to coordinate based on affective states rather than rigid algorithms. In action recognition, Zheng proposed the PM-STGCN (Posture Motion-based Spatiotemporal Fused Graph Convolutional Network), which fuses joint and motion information for more accurate skeleton-based gesture understanding. Though early in his career, his work has already garnered citations from researchers in robotics, AI, and cognitive science, and his integrated approach—merging emotional modeling with physical control—positions him as a rising voice in the quest for truly collaborative, human-aware robots.