Pengyao Xu
Papers
2
Total Citations
12
H-Index
2
About
Pengyao Xu is a rising researcher in field robotics, specializing in autonomous navigation and motion control for wheeled mobile robots operating in challenging, unstructured terrains. His work addresses critical problems in planetary exploration and off-road robotics, where wheel slippage on loose soil poses significant risks to mission safety and control accuracy. Xu’s most-cited paper (2024, 10 citations) introduces a novel few-shot learning approach to estimate wheel slippage from wheel-rut images, enabling robots to adapt to unfamiliar soil conditions without extensive training data—a breakthrough for planetary rovers. More recently, he has advanced robotic arm trajectory planning in dynamic environments using a self-optimizing replay mechanism within deep reinforcement learning (2025, 2 citations), overcoming the high-dimensional uncertainty and real-time adaptation challenges that plague traditional DRL methods. By sidestepping the need for human expert strategies, Xu’s work pushes toward fully autonomous, self-improving manipulation systems. His research sits at the intersection of computer vision, reinforcement learning, and field robotics, with clear applications in space exploration, disaster response, and agricultural automation. Though early in his career, Xu’s focus on practical, data-efficient solutions marks him as a promising innovator in adaptive robotic systems.
Research Focus
Key Achievements
Top Papers
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- 2