Zijing Chi

Papers

Total Citations

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About

Zijing Chi is a researcher focused on advancing robotic perception and human-robot interaction through deep learning and motion planning. Their work centers on enabling robots to operate safely and autonomously in dynamic environments, particularly by improving how machines perceive and react to human presence. Chi’s most cited paper, “3D Pose Estimation of Robot Arm with RGB Images Based on Deep Learning” (12 citations), introduces a method for accurately determining a robot arm’s spatial configuration using only standard visual data, a critical step for intuitive human-robot collaboration. In “Dynamic Motion Planning Algorithm in Human-Robot Collision Avoidance” (6 citations), they developed real-time strategies to prevent physical contact during cooperative tasks, while “A Collision-Free Path Planning Method Using Direct Behavior Cloning” (4 citations) leverages imitation learning to generate safe trajectories from expert demonstrations. Together, these contributions address key challenges in industrial and service robotics, offering practical solutions for safer, more adaptable automation. Chi’s work demonstrates a clear commitment to bridging perception and control, with potential applications in manufacturing, healthcare, and assistive technologies.