31 citations · 47 across the 6 of their papers we have counts for
8 papers
CppFlow: Generative Inverse Kinematics for Efficient and Robust Cartesian Path Planning
Jeremy Morgan, David Millard, Gaurav S. Sukhatme
In this work we present CppFlow - a novel and performant planner for the Cartesian Path Planning problem, which finds valid trajectories up to 129x faster than current methods, whi…
Granular Gym: High Performance Simulation for Robotic Tasks with Granular Materials
David Millard, Daniel Pastor, Joseph Bowkett +2
Granular materials are of critical interest to many robotic tasks in planetary science, construction, and manufacturing. However, the dynamics of granular materials are complex and…
Probabilistic Inference of Simulation Parameters via Parallel Differentiable Simulation
Eric Heiden, Christopher E. Denniston, David Millard +2
To accurately reproduce measurements from the real world, simulators need to have an adequate model of the physical system and require the parameters of the model be identified. We…
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…