Papers
2
Total Citations
19
H-Index
2
About
Xu Lu is a researcher at the forefront of intelligent robotics, with key contributions spanning mobile robot navigation and tactile perception. In a foundational 2017 work, Lu introduced a multi-step reinforcement learning algorithm that integrates virtual potential fields for mobile robot path planning, a method that has garnered 16 citations for its practical approach to autonomous navigation. More recently, Lu has tackled one of the most pressing challenges in robotic perception: continual learning in dynamic environments. In the 2024 paper "TactCLNet," Lu proposed a generative replay-based continual learning network for object hardness recognition via tactile sensing, addressing the critical issue of catastrophic forgetting in deep neural networks. This work, already accumulating 3 citations, demonstrates Lu’s ability to push the boundaries of robotic tactile perception, enabling robots to adapt and learn continuously in open, real-world settings. With a research focus that bridges classical path planning and cutting-edge continual learning, Xu Lu is shaping the future of adaptive, perceptive robotics.
Research Focus
Key Achievements
Top Papers
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