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.
Research Projects
A collection of research projects I have contributed to. My work mainly focuses on active perception and high level control.
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
