Yiqin Yang

Weihai Science and Technology Bureau, Tsinghua University, Chinese Academy of Sciences

Papers

Total Citations

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About

Yiqin Yang is a robotics and artificial intelligence researcher whose work spans mobile robot control systems and advanced reinforcement learning methodologies. With a foundation in practical robotics engineering, Yang made early contributions to wheeled mobile robot design, developing a two-wheeled robot platform utilizing PID control systems that has garnered 30 citations and demonstrated clear real-world applicability in the growing service robotics sector. Building on this hardware-grounded perspective, Yang has more recently turned to the theoretical and algorithmic challenges of reinforcement learning, particularly in contexts where data collection is costly or hazardous. Their work on Offline Reinforcement Learning with Uncertain Action Constraints (UAC) addresses a critical barrier to deploying RL in autonomous driving and robotic systems, earning 6 citations since its 2023 publication. Complementing this, Yang's research on auxiliary reward generation through transition distance representation learning tackles the persistent challenge of reward function design in real-world sequential decision-making, reflecting a sophisticated understanding of RL's practical limitations. Collectively, Yang's research trajectory reveals a researcher bridging physical robotics and cutting-edge machine learning, with a consistent focus on making intelligent systems more reliable, safe, and deployable in demanding real-world environments.