Papers
25
Total Citations
400
H-Index
13
About
Bidan Huang is a robotics researcher whose work spans continuum robotics, robotic manipulation, tactile sensing, and human-robot interaction. His research addresses some of the most pressing challenges in modern robotics: enabling machines to perceive, learn, and act intelligently in complex real-world environments. Huang has made notable contributions to robot grasping and manipulation, from early work on real-time grasp learning strategies (41 citations) to leveraging large-scale foundation models for grasp detection through the influential Grasp-Anything dataset (36 citations). His investigations into tactile sensing are particularly significant — developing graph neural network frameworks for dexterous in-hand manipulation without visual feedback (TacGNN, 26 citations) and pioneering sim-to-real transfer incorporating tactile sensory information (27 citations). In medical robotics, his recent work on body contact estimation for continuum robots (45 citations, his most-cited paper) addresses critical safety challenges in endoluminal surgical interventions. Huang has also advanced multi-robot cooperation through learning-by-demonstration frameworks and task-priority redundancy resolution for dual-arm systems. His cumulative body of work, spanning surgical robotics, manipulation learning, and sensory integration, reflects a researcher deeply committed to bridging theoretical robotics with practical, safety-conscious applications — making his profile essential reading for students pursuing intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Learning a real time grasping strategy41 citations · 2013
- 3Grasp-Anything: Large-scale Grasp Dataset from Foundation Models36 citations · 2024
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- 5Sim-to-Real Transfer for Robotic Manipulation with Tactile Sensory27 citations · 2021
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