Yujia Wang

Shanghai University of Engineering Science, Shanghai Jiao Tong University, Harbin University

Papers

Total Citations

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About

Yujia Wang is a robotics and artificial intelligence researcher whose work bridges classical path planning with modern reinforcement learning. Her research focuses on mobile robot navigation, motion control systems, and intelligent exploration strategies for autonomous agents. Wang's most influential contribution is her 2015 paper on "Robot Path Planning Based on Random Coding Particle Swarm Optimization" (15 citations), which introduced an innovative approach to solving the fundamental challenge of finding collision-free optimal paths for mobile robots. She further advanced the field with her 2021 work on "Population-Guided Novelty Search for Reinforcement Learning in Hard Exploration Environments" (9 citations), a parallel learning method that addresses critical limitations in RL—including inadequate exploration, sparse rewards, and deceptive reward functions. Earlier in her career, Wang contributed to practical robotics through her 2006 study on platform and motion control systems for wheeled mobile robots, developing a two-wheel differential drive architecture using embedded PC/104 computers. Her work demonstrates a consistent commitment to solving real-world robotics challenges, from foundational control systems to cutting-edge exploration algorithms that push the boundaries of autonomous learning in complex environments.