Papers
5
Total Citations
17
H-Index
3
About
Jiying Wu is a rising researcher in robotics and autonomous systems, with a focus on intelligent control for mobile and aerial manipulators. Their work addresses critical challenges in trajectory tracking, autonomous navigation, and aerial physical interaction. Notably, Wu developed a self-adaptive double Q-backstepping approach based on reinforcement learning to enhance trajectory accuracy for mobile robots in complex indoor environments, a method that improves high-zoom image capture tasks. They also pioneered a learning-based framework for autonomous navigation in unmapped, unknown spaces using only low-precision sensors, advancing the field of mapless robot mobility. Wu’s contributions extend to aerial robotics with the design of a novel aerial manipulator featuring a front cutting effector for physical interaction tasks, such as tree pruning, and they have explored reinforcement learning for floating target tracking with airborne robotic arms. With over 17 citations across their most-cited works, including a 2023 paper on model predictive control (later retracted), Wu’s research demonstrates a commitment to integrating learning and control for real-world robotic applications. Their work is particularly relevant for students and researchers interested in reinforcement learning, autonomous navigation, and aerial manipulation.
Research Focus
Key Achievements
Top Papers
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- 2Learning Autonomous Navigation in Unmapped and Unknown Environments5 citations · 2024
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