Papers
9
Total Citations
160
H-Index
6
About
Ruohan Gao is redefining how machines perceive and interact with the world by pioneering multisensory object-centric learning. Her research fuses vision, sound, and touch to create richer, more realistic representations of objects for robotics and AI. Gao’s most significant contribution is the **ObjectFolder** series—a groundbreaking dataset and benchmark that virtualizes objects with synchronized visual, acoustic, and tactile properties. The flagship ObjectFolder 2.0 (59 citations) and the ObjectFolder Benchmark (24 citations) enable sim-to-real transfer and provide 10 standardized tasks for multisensory recognition, reconstruction, and manipulation. She further advanced robotic dexterity with **See, Hear, and Feel** (9 citations), demonstrating how smart sensory fusion improves manipulation outcomes. Gao also bridges AI and human-computer interaction through **NOIR** (8 citations), a brain-robot interface that lets users command everyday tasks via neural signals. Her work on differentiable physics simulation (30 citations) and sound rendering (DiffSound) pushes the boundaries of physically grounded neural object modeling. With over 160 total citations and a clear trajectory toward embodied intelligence, Gao is a rising leader in multisensory AI and robotic perception.
Research Focus
Key Achievements
Top Papers
- 1ObjectFolder 2.0: A Multisensory Object Dataset for Sim2Real Transfer59 citations · 2022
- 2Differentiable Physics Simulation of Dynamics-Augmented Neural Objects30 citations · 2023
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- 5See, Hear, and Feel: Smart Sensory Fusion for Robotic Manipulation9 citations · 2022
- 6NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities8 citations · 2023
- 7ObjectFolder 2.0: A Multisensory Object Dataset for Sim2Real Transfer3 citations · 2022
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- 9Differentiable Physics Simulation of Dynamics-Augmented Neural Objects2 citations · 2022