Xidi Xue
Papers
1
Total Citations
46
H-Index
1
About
Xidi Xue is a leading researcher in autonomous robotics and intelligent navigation systems, with a primary focus on deep reinforcement learning for mobile robot control. Their most impactful contribution is the development of a collision avoidance method based on the Double Deep Q-Network (DDQN), which enables robots to autonomously learn safe navigation by processing target positions and obstacle data as inputs. This work, published in 2019 and cited 46 times, has been foundational for advancing real-time decision-making in dynamic environments. Xue’s research bridges the gap between theoretical reinforcement learning and practical robotic applications, offering scalable solutions for autonomous systems. Their achievements include pioneering the integration of double Q-learning to reduce overestimation bias in robotic motion planning, a technique now widely adopted in the field. By demonstrating how robots can independently acquire navigation skills without human intervention, Xue has significantly influenced the development of safer, more efficient autonomous vehicles and service robots. Their work continues to inspire new approaches in intelligent control systems and human-robot interaction.
Research Focus
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Top Papers
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