Papers
3
Total Citations
10
H-Index
2
About
Zhaorun Chen is a robotics researcher whose work bridges bionic design, human-robot interaction, and reinforcement learning. His research focuses on developing intelligent robotic systems that can operate safely and efficiently alongside humans, with particular emphasis on manipulator control and motion planning. Chen's most cited work, "A Bionic Arm Mechanism Design and Kinematic Analysis of the Humanoid Traffic Police" (2019, 5 citations), presents a cost-efficient 3D-printed robotic arm capable of executing traffic command gestures, demonstrating practical applications of biomimetic design in public service. Building on this foundation, his paper "Simulation of Real-time Collision-Free Path Planning Method with Deep Policy Network in Human-Robot Interaction Scenario" (2023, 3 citations) addresses the critical challenge of dynamic collision avoidance, proposing deep reinforcement learning approaches that outperform traditional methods like RRT in real-time human-robot collaboration settings. Chen's work "Efficiently Training On-Policy Actor-Critic Networks in Robotic Deep Reinforcement Learning with Demonstration-like Sampled Exploration" (2021, 2 citations) tackles the sample efficiency problem in high-dimensional RL environments by integrating expert demonstration principles into exploration strategies. His contributions advance the practical deployment of robots in human-centric environments, from traffic management to collaborative manufacturing.
Research Focus
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Top Papers
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