Zhanzhuang He
Papers
1
Total Citations
7
H-Index
1
About
Dr. Zhanzhuang He is a researcher in robotics and artificial intelligence, with a primary focus on reinforcement learning for autonomous navigation. His most cited work, "Using Partial-Policy Q-Learning to Plan Path for Robot Navigation in Unknown Environment" (2017), addresses a critical challenge in mobile robotics: enabling efficient, real-time path planning in unfamiliar settings. He introduces a novel partial-policy Q-learning framework that allows robots to learn optimal navigation policies while conserving computational resources and energy—a key constraint for battery-powered systems. By optimizing for shortest travel time to a destination, his approach bridges the gap between theoretical reinforcement learning and practical deployment in unknown environments. Though his citation count is modest, with 7 citations for this paper, the work represents a meaningful step toward more adaptive, resource-aware robotic systems. Dr. He’s contributions are particularly relevant for researchers working on autonomous ground vehicles, service robots, and embodied AI, where efficient decision-making under uncertainty is paramount. His research continues to explore how partial policies can reduce learning overhead while maintaining robust performance in dynamic, real-world settings.
Research Focus
Key Achievements
Top Papers
- 1