Weifan Wang

Papers

Total Citations

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About

Weifan Wang is a roboticist whose research centers on human-robot interaction, with a particular focus on seamless and intuitive handover tasks. His work addresses the critical challenge of enabling robots to perceive and respond to human actions in real time, bridging the gap between raw sensory data and dexterous manipulation. Wang’s most-cited paper, "Human-to-robot handovers based on multimodal perception" (2025), proposes a framework that fuses visual and tactile cues to improve the safety and fluidity of object transfers—a foundational step for collaborative robotics in domestic and industrial settings. His earlier work, "6-DoF Grasp Planning on Point Cloud for Human-to-Robot Handover Task" (2023), introduces a method for generating stable grasps directly from 3D point cloud data, enabling robots to handle objects of varying shapes and orientations without prior models. While still early in his career, with citations accumulating steadily, Wang’s contributions are notable for their practical emphasis on real-world deployment. His research has been presented at leading robotics venues, and he is recognized for advancing multimodal perception as a key enabler of safe, adaptive human-robot collaboration.