Kaidong Zhao

University of Chinese Academy of Sciences, Guangdong Institute of Intelligent Manufacturing

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Total Citations

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About

Kaidong Zhao is a rising researcher in robotics and artificial intelligence, with a primary focus on multi-robot navigation and reinforcement learning. His work addresses critical challenges in dynamic environments, where traditional algorithms like Dijkstra and A* often fall short. Zhao’s 2022 paper, “Hybrid Navigation Method for Multiple Robots Facing Dynamic Obstacles,” has garnered 19 citations for its innovative approach to enabling robots to adapt in real-time to unpredictable obstacles, advancing the field of autonomous coordination. In 2025, he introduced “Sample-efficient backtrack temporal difference deep reinforcement learning,” which has already earned 18 citations by improving learning efficiency in complex decision-making tasks—a key step toward more practical AI systems. Zhao’s contributions are notable for bridging theory and application, offering scalable solutions that enhance robot autonomy in real-world settings. His work is highly relevant for students and researchers exploring intelligent navigation and efficient reinforcement learning, positioning him as a promising voice in next-generation robotics.