Guangze Zhang
Papers
2
Total Citations
5
H-Index
2
About
Guangze Zhang is a researcher at the forefront of human-robot collaboration, with a focus on safe and efficient interaction in specialized medical environments. His primary research areas encompass deep reinforcement learning for motion planning, behavior recognition, and the development of collaborative datasets for human-robot systems. Zhang's major contribution lies in addressing the critical challenge of collision avoidance during human-robot collaboration in pathology examination scenes. He pioneered a deep reinforcement learning-based motion planner that transforms robotic arm navigation into a Markov decision process, effectively preventing collisions between robotic and human arms during delicate pathological experiments. His work is further supported by the creation of the PEC dataset, a specialized resource for behavior recognition in pathology examination scenes, which has already garnered attention with 2 citations. With his papers accumulating over 5 citations in just two years, Zhang is establishing himself as a rising expert in applying AI to real-world medical robotics, demonstrating how intelligent systems can enhance safety and precision in clinical settings.
Research Focus
Key Achievements
Top Papers
- 1
- 2