31 citations · 47 across the 6 of their papers we have counts for
4 papers · 1 filter
NeuralSim: Augmenting Differentiable Simulators with Neural Networks
Eric Heiden, David Millard, Erwin Coumans +2
Differentiable simulators provide an avenue for closing the sim-to-real gap by enabling the use of efficient, gradient-based optimization algorithms to find the simulation paramete…
Domain Adversarial Neural Networks for Dysarthric Speech Recognition
Dominika Woszczyk, Stavros Petridis, David Millard
Speech recognition systems have improved dramatically over the last few years, however, their performance is significantly degraded for the cases of accented or impaired speech. Th…
Augmenting Differentiable Simulators with Neural Networks to Close the Sim2Real Gap
Eric Heiden, David Millard, Erwin Coumans +1
We present a differentiable simulation architecture for articulated rigid-body dynamics that enables the augmentation of analytical models with neural networks at any point of the…
Automatic Differentiation and Continuous Sensitivity Analysis of Rigid Body Dynamics
David Millard, Eric Heiden, Shubham Agrawal +1
A key ingredient to achieving intelligent behavior is physical understanding that equips robots with the ability to reason about the effects of their actions in a dynamic environme…