Papers
2
Total Citations
5
H-Index
2
About
Chenchen Liu is a rising researcher in robotics and artificial intelligence, specializing in autonomous navigation and robotic manipulation. Their work focuses on developing intelligent systems that enable robots to operate effectively in complex, real-world environments. A key contribution is the DPDQN-TER algorithm, an improved deep reinforcement learning approach for mobile robot path planning in dynamic scenarios, such as large-scale structure assembly measurement. This method enhances stability and efficiency in obstacle-dense settings, addressing critical challenges in industrial inspection and quality control tasks. Liu has also advanced robotic grasping by learning from human demonstration through contact analysis, moving beyond geometry-based methods to improve dexterity with parallel grippers. With early citation counts of 3 and 2 for their most-cited papers, Liu’s research is gaining traction for its practical impact. Their work bridges the gap between theoretical reinforcement learning and applied robotics, offering scalable solutions for automation. As a contributor to cutting-edge robotic intelligence, Chenchen Liu is shaping the future of adaptive, human-inspired robotic systems.
Research Focus
Key Achievements
Top Papers
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- 2