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About

Chengliang Zhong is a rising researcher at the forefront of embodied AI, computer vision, and robotics, with a particular focus on bridging language, perception, and manipulation. His work is defined by a deep commitment to enabling robots to understand and interact with the physical world through natural language. Zhong's most impactful contribution, the GaussianGrasper system (2024, 37 citations), constructs a 3D scene representation using Gaussian splatting that can accommodate open-vocabulary language queries, allowing robots to grasp objects based on human directives without pre-defined categories. This work represents a significant step toward flexible, human-robot collaboration. He also contributed a comprehensive overview of deep learning applications in visual SLAM (2022, 38 citations), a foundational survey that has guided subsequent research in autonomous navigation. More recently, his IAMTrack (2025) advances RGBT tracking by introducing inter-frame appearance and modality token propagation with temporal modeling. With a rapidly growing citation record and a focus on integrating language, 3D scene understanding, and robotic manipulation, Zhong is establishing himself as a key voice in the next generation of intelligent, interactive robotics.