Yongxiang Fan

About

Yongxiang Fan is a robotics researcher whose work spans robot learning, manipulation, grasping, and human-robot interaction. His research addresses some of the most practically demanding challenges in modern robotics: enabling machines to learn from human demonstration, plan complex grasps, and operate safely alongside people. Fan's most influential contribution — "Teach Industrial Robots Peg-Hole-Insertion by Human Demonstration" (2016, 79 citations) — pioneered a learning-from-demonstration approach to robotic assembly, allowing robots to acquire nuanced force-control skills directly from human teachers rather than through painstaking manual programming. This work reflects a broader theme in his research: bridging human dexterity and robotic precision. His studies on dexterous manipulation, including multi-fingered finger gaits planning and robust manipulation under dynamic uncertainty, have collectively accumulated over 46 citations, advancing the field's understanding of how robots can handle objects reliably in unstructured environments. Fan has also contributed meaningfully to robot safety, with his real-time collision avoidance algorithm (33 citations) offering practical solutions for human-robot collaboration. His more recent deep learning work on 6-DoF grasp proposal networks demonstrates his adaptability to emerging AI-driven paradigms. Across more than a decade of research, Fan's output reflects a consistent commitment to making robots more capable, safe, and teachable in real-world industrial settings.

Research Focus

Key Achievements

Top Papers

1. 1
   Teach industrial robots peg-hole-insertion by human demonstration

   79 citations · 2016
2. 2
   State estimation for deformable objects by point registration and dynamic simulation

   41 citations · 2017
3. 3
   Real-time collision avoidance algorithm on industrial manipulators

   33 citations · 2017
4. 4
   6-DoF Contrastive Grasp Proposal Network

   25 citations · 2021
5. 5
   Efficient Grasp Planning and Execution With Multifingered Hands by Surface Fitting

   25 citations · 2019
6. 6
   Real-Time Finger Gaits Planning for Dexterous Manipulation

   21 citations · 2017
7. 7
   Human guidance programming on a 6-DoF robot with collision avoidance

   20 citations · 2016
8. 8
   Robust dexterous manipulation under object dynamics uncertainties

   13 citations · 2017
9. 9
   Real-time robust finger gaits planning under object shape and dynamics uncertainties

   12 citations · 2017
10. 10
    Robot Grasp Planning: A Learning from Demonstration-Based Approach

    10 citations · 2024

Key Collaborators

Contact & Links