Papers
14
Total Citations
357
H-Index
9
About
Wenbing Huang is a leading researcher in robotic manipulation, tactile sensing, and intelligent grasping. His work bridges the gap between perception and action, developing systems that enable robots to interact with their environment more dexterously and adaptively. Huang is best known for his pioneering contributions to multimodal grasping, including the design of a "Multimode Grasping Soft Gripper" (119 citations) that combines layer jamming and tendon-driven mechanisms to handle diverse objects. He has also made significant advances in tactile recognition, proposing novel deep learning architectures like the 3T-RTCN (29 citations) and LDS-FCM (19 citations) for spatio-temporal tactile data analysis. His research on integrating visual and tactile sensing for object classification and grasp planning (77 citations) has been highly influential, as has his work on learning to grasp familiar objects using shape affordance (32 citations). Beyond manipulation, Huang has explored embodied visual navigation, including echo-enhanced navigation in poor visibility (17 citations) and active camera control for multi-object navigation (8 citations). His work on elastic tactile simulation (21 citations) addresses the critical challenge of data scarcity in tactile robotics. With over 300 total citations, Huang’s research is shaping the future of autonomous robotic systems that can perceive, reason, and act in complex, unstructured environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2Object Classification and Grasp Planning Using Visual and Tactile Sensing77 citations · 2016
- 3
- 4
- 5Elastic Tactile Simulation Towards Tactile-Visual Perception21 citations · 2021
- 6
- 7Echo-Enhanced Embodied Visual Navigation17 citations · 2023
- 8
- 9Task Transfer by Preference-Based Cost Learning9 citations · 2019
- 10Learning Active Camera for Multi-Object Navigation8 citations · 2022