Learning Material Properties using a Differentiable Simulator for Liquid/Granular
Gautham Narayan, Xingyu Lin, David Held
Currently training a Material Point Method based differentiable
simulator to learn material properties. Cross Entropy Method has shown
good performance for trajectory optimization once the material properties have been learnt.
More details coming soon ...
Self-supervised Transparent Liquid Segmentation for Robotic Pouring
Gautham Narayan, Kai Zhang, Ben Eisner, Xingyu Lin, David Held
ICRA 2022 and abridged at NeurIPS 2021 Deep
A novel segmentation pipeline that can segment transparent
as water from a static,
RGB image without requiring any manual annotations. We show that this system can run in
and aid in tasks such
as robotic pouring.
ROLL: Visual Self-Supervised Reinforcement Learning with Object Reasoning
Yufei Wang*, Gautham Narayan*, Xingyu Lin, Brian Okorn, David Held
* denotes equal contribution
Conference on Robot
Unknown object segmentation to learn a visual representation
reason about occlusions. Our method achieves
state of the art on object manipulation benchmarking tasks.
Segmentation for learning image based goal conditioned policies
Gautham Narayan, David Held
Master's Thesis - Carnegie Mellon University, 2020
Experimental Droplet Spatter Analysis Using Least Squares Approximation
Gautham Narayan, Bill Eddy
Internal Report - NIST Center of Excellence in Forensic Science, 2020
Effect of winglets induced tip vortex structure on the performance of subsonic
Gautham Narayan, Bibin John
Elsevier - Aerospace Science and Technology, 2016
Design optimization for subsonic winglets using computational fluid dynamics.