Papers
35
Total Citations
308
H-Index
10
About
Hongsheng He is a robotics researcher whose work spans physical human-robot interaction, dexterous robotic grasping, social robotics, and robot perception. His early and highly cited contributions include pioneering neural-network-based approaches to estimating human motion intention for physical human-robot interaction (2011, 37 citations), laying groundwork for more intuitive and responsive collaborative robots. He has made significant strides in intelligent robotic grasping, developing systems that leverage natural-language object descriptions to determine grasp strategies (2018, 27 citations) and the context-aware "MagicHand" system, which incorporates object properties such as fragility and texture to enable dexterous manipulation (2020, 17 citations). His repeated contributions to the Social Robotics conference series reflect a sustained commitment to advancing socially intelligent machines, including bio-inspired saccadic attention systems for robotic heads (2013, 16 citations). More recently, He has expanded into robot perception and calibration, introducing adaptive camera-LiDAR fusion for depth estimation (2023, 13 citations) and unified visual-inertial and robotic-arm calibration frameworks (2021, 13 citations). His research on neural-logic learning further enables robots to reason about spatial relationships with human-like intuition (2020, 15 citations), collectively positioning him as a versatile and impactful contributor to modern intelligent robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2Social Robotics34 citations · 2016
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- 6Social Robotics16 citations · 2017
- 7Robotic Understanding of Spatial Relationships Using Neural-Logic Learning15 citations · 2020
- 8
- 9Adaptive Active Fusion of Camera and Single-Point LiDAR for Depth Estimation13 citations · 2023
- 10Social Robotics12 citations · 2019