Papers
4
Total Citations
43
H-Index
3
About
Weiqi Wang is a robotics researcher whose work spans human-robot interaction, semantic scene understanding, and power infrastructure maintenance. His most cited paper, "Towards Skill Transfer via Learning-Based Guidance in Human-Robot Interaction" (2019, 20 citations), introduces a novel framework for transferring surgical drilling skills from human demonstration to robotic systems, with direct applications in orthopaedic surgery. In semantic mapping, Wang's "Combining ElasticFusion with PSPNet for RGB-D Based Indoor Semantic Mapping" (2018, 13 citations) pioneered the fusion of geometric SLAM with deep semantic segmentation, enabling robots to perceive both spatial structure and object identities in complex indoor environments. His more recent "LiDAR-Based Real-Time Panoptic Segmentation via Spatiotemporal Sequential Data Fusion" (2022, 7 citations) advances this work by unifying semantic and instance segmentation for real-time outdoor navigation. Wang also addresses critical challenges in power grid maintenance, analyzing arc discharge phenomena during live-line maintenance on 500 kV transmission lines (2025). His research demonstrates a consistent focus on equipping robots with richer perceptual and interactive capabilities, from surgical precision to large-scale infrastructure inspection, with applications spanning healthcare, autonomous navigation, and industrial robotics.
Research Focus
Key Achievements
Top Papers
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- 2Combining ElasticFusion with PSPNet for RGB-D Based Indoor Semantic Mapping13 citations · 2018
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