Xunman Chen

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About

Xunman Chen is a robotics researcher whose work centers on advancing the practical capabilities of dual-arm robotic systems, with a particular focus on the Baxter robot platform. Chen's major contributions lie in developing vision-guided manipulation strategies for complex assembly tasks, most notably demonstrated in the highly cited work "Vision-guided peg-in-hole assembly by Baxter robot" (2017, 25 citations). This research introduced a step-by-step grasping strategy combined with vision-guidance methods, enabling the Baxter robot to perform precise peg-in-hole assembly—a fundamental challenge in automated manufacturing. The work's impact is reflected in its citation count, establishing it as a key reference for researchers tackling similar robotic assembly problems. Additionally, Chen's earlier work on kinematic calibration and vision-based object grasping (2016) laid the groundwork for improving Baxter's positioning accuracy and object manipulation capabilities. Through these contributions, Chen has helped bridge the gap between industrial robotic assembly requirements and the capabilities of accessible dual-arm platforms, making significant strides in practical robotic manipulation that continue to inform current research in automated manufacturing and human-robot collaboration.