Wenzhen Yuan

About

Wenzhen Yuan is a pioneering robotics researcher whose work sits at the intersection of tactile sensing, robot perception, and manipulation. She is best known for her foundational contributions to the GelSight sensor platform, a high-resolution tactile technology that enables robots to perceive fine surface geometry, shear forces, and slip with remarkable precision. Her 2017 paper on GelSight has accumulated over 1,100 citations, establishing it as a landmark reference in robot sensing, while her 2021 work on soft magnetic skin for super-resolution tactile sensing has garnered over 500 citations, demonstrating her continued innovation in the field. Beyond hardware development, Yuan has made significant strides in integrating tactile feedback with machine learning and vision. Her research on learning to grasp and regrasp using vision and touch, cloth texture recognition through multi-modal sensing, and hardness estimation via deep learning illustrates her commitment to building robots that perceive and interact with the world more like humans do. She has also contributed to the emerging challenge of deformable object manipulation, broadening the scope of intelligent robotic systems. With over 3,300 combined citations across her top works, Yuan's research has profoundly shaped how the robotics community thinks about touch as an essential sensing modality.

Research Focus

Key Achievements

Top Papers

1. 1
   GelSight: High-Resolution Robot Tactile Sensors for Estimating Geometry and Force

   1,102 citations · 2017
2. 2
   Soft magnetic skin for super-resolution tactile sensing with force self-decoupling

   522 citations · 2021
3. 3
   More Than a Feeling: Learning to Grasp and Regrasp Using Vision and Touch

   353 citations · 2018
4. 4
   Measurement of shear and slip with a GelSight tactile sensor

   277 citations · 2015
5. 5
   Localization and manipulation of small parts using GelSight tactile sensing

   254 citations · 2014
6. 6
   Challenges and Outlook in Robotic Manipulation of Deformable Objects

   244 citations · 2022
7. 7
   Shape-independent hardness estimation using deep learning and a GelSight tactile sensor

   165 citations · 2017
8. 8
   ViTac: Feature Sharing Between Vision and Tactile Sensing for Cloth Texture Recognition

   142 citations · 2018
9. 9
   Active Clothing Material Perception Using Tactile Sensing and Deep Learning

   138 citations · 2018
10. 10
    3D Shape Perception from Monocular Vision, Touch, and Shape Priors

    118 citations · 2018

Key Collaborators

Contact & Links