Papers
29
Total Citations
1,138
H-Index
14
About
Yuzhe Qin is a robotics and computer vision researcher whose work centers on dexterous robotic manipulation, simulation environments, and multimodal sensing. He is perhaps best known for co-creating SAPIEN (2020), a physically realistic simulated environment for part-based interactive objects that has become a foundational benchmark in the field, amassing over 370 citations. His research has consistently pushed the boundaries of what robot hands can do, with influential contributions spanning imitation learning from human demonstrations, teleoperation systems, and tactile sensing. Qin's DexMV and single-camera teleoperation works (2022) demonstrated elegant approaches to transferring human manipulation skills to multi-finger robot hands, collectively earning over 200 citations. His AnyTeleop system (2023) generalized vision-based teleoperation across diverse robot hardware, while his tactile sensing research — including "Rotating without Seeing" and "Robot Synesthesia" — explores how touch and vision together enable sophisticated in-hand dexterity previously thought exclusive to humans. His early work on SE(3) grasp detection further established his versatility across the manipulation pipeline. With research conducted across leading institutions and industry internships including NVIDIA, Qin's cumulative impact exceeds 900 citations, making him a significant emerging voice shaping the future of capable, generalizable robotic systems.
Research Focus
Key Achievements
Top Papers
- 1SAPIEN: A SimulAted Part-Based Interactive ENvironment373 citations · 2020
- 2DexMV: Imitation Learning for Dexterous Manipulation from Human Videos117 citations · 2022
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- 5Rotating without Seeing: Towards In-hand Dexterity through Touch70 citations · 2023
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- 10Robot Synesthesia: In-Hand Manipulation with Visuotactile Sensing32 citations · 2024