Zhenquan He

Papers

Total Citations

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About

Zhenquan He is a researcher at the intersection of robotics and computer vision, with key contributions in programming by demonstration (PbD), 3D point cloud analysis, and 6DoF pose estimation. His most cited work, "Joining Force of Human Muscular Task Planning With Robot Robust and Delicate Manipulation for Programming by Demonstration" (2020, 22 citations), addresses a critical challenge in PbD: recognizing high-fidelity finger movements from human demonstrators to enable robots to perform both robust and delicate manipulations. This work bridges human motor planning and robotic execution, advancing intuitive robot programming for industrial applications. He further explores deep learning for 3D data in "Deep Neural Network for Point Sets Based on Local Feature Integration" (2022, 3 citations), focusing on object classification and part segmentation using point clouds—a key modality for robotics and virtual reality. In "LHFF-Net: Local heterogeneous feature fusion network for 6DoF pose estimation" (2021, 2 citations), he develops a network that fuses local geometric and color features to accurately estimate an object’s full 6-degree-of-freedom pose, essential for robotic grasping and augmented reality. Though early in his career, He’s work demonstrates a clear trajectory toward enabling robots to perceive and interact with the physical world more naturally and precisely.