Papers
13
Total Citations
1,850
H-Index
11
About
Siyuan Dong is a robotics researcher whose work sits at the intersection of tactile sensing, robot perception, and dexterous manipulation. She is best known for her foundational contributions to high-resolution tactile sensor development, most notably through the GelSight and GelSlim sensor families. Her 2017 paper on GelSight — amassing over 1,100 citations — established a landmark framework for estimating contact geometry and force using soft, vision-based tactile surfaces, fundamentally reshaping how robots perceive their physical environment. Building on this, her GelSlim 3.0 system (211 citations) advanced the field by integrating shape, force, and slip detection into a compact form factor suited for real-world bin-picking tasks. Dong's research extends beyond sensor hardware into intelligent manipulation strategies. Her work on cable manipulation with tactile-reactive grippers (225 citations) demonstrates how rich tactile feedback can enable robots to handle complex, deformable objects with remarkable agility. Her slip detection research, combining tactile and visual modalities through deep learning, further exemplifies her commitment to multimodal perception. Across her body of work, Dong has consistently bridged hardware innovation and machine learning, producing tools and methods that are widely adopted across the robotics research community.
Research Focus
Key Achievements
Top Papers
- 1GelSight: High-Resolution Robot Tactile Sensors for Estimating Geometry and Force1,102 citations · 2017
- 2Cable manipulation with a tactile-reactive gripper225 citations · 2021
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- 4Slip Detection with Combined Tactile and Visual Information155 citations · 2018
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- 9Slip Detection with Combined Tactile and Visual Information14 citations · 2018
- 10Cable Manipulation with a Tactile-Reactive Gripper11 citations · 2019