Sang-Hoon Yang

About

Sang-Hoon Yang is a robotics researcher whose work centers on tactile perception for dexterous robotic manipulation. His most-cited paper, "In-Hand Object Classification and Pose Estimation With Sim-to-Real Tactile Transfer for Robotic Manipulation" (2023, 7 citations), tackles a critical challenge in robotics: enabling robots to sense and understand objects through touch. Yang's key contribution is a sim-to-real transfer approach that overcomes the difficulty of generating rich tactile data in real-world settings. By training tactile perception models in simulation and transferring them to physical robots, his method allows for accurate in-hand object classification and pose estimation without extensive real-world data collection. This work bridges the gap between simulated training and real-world robotic dexterity, offering a practical pathway for robots to handle delicate objects with greater precision. Yang's research has implications for manufacturing, healthcare, and assistive robotics, where tactile feedback is essential. His approach exemplifies how simulation can accelerate the development of robust robotic perception systems, making him a notable contributor to the field of robotic manipulation and tactile sensing.

Research Focus

Key Achievements

Top Papers

1. 1
   In-Hand Object Classification and Pose Estimation With Sim-to-Real Tactile Transfer for Robotic Manipulation

   7 citations · 2023

Key Collaborators

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