You've Got to Feel It To Believe It: Multi-Modal Bayesian Inference for Semantic and Property Prediction
We incorporate both visual and tactile information to improve semantic maps for robots.
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Yuzhen Chen
I am a master's student in the Computational Science and Engineering program at Harvard. I am a member of the MIT Media Lab (Multisensory Intelligence), supervised by Prof. Paul Liang.
I completed my undergraduate studies in Computer Engineering at the University of Michigan, where I was a member of the ROAHM Lab under the supervision of Prof. Ram Vasudevan.
Research Projects
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Differentiable Discrete Elastic Rods for Real-Time Modeling of Deformable Linear Objects
We develop the DEFORM modle to predict the real time position and pose of single branch elastic rods.
Multi-Modal Semantic Perception Using Bayesian Inference
We Leverage multiple sensing modalities to improve the prediction of semantic classifications and their physical properties.
MatPredict: a dataset and benchmark for learning material properties of diverse indoor objects
Learning Physical Object Properties from Synthetic Images Using Diffusion Model
DEFT: Differentiable Branched Discrete Elastic Rods for Modeling Furcated DLOs in Real-Time
Real-Time Modeling for Elastic Rods