69 citations · 98 across the 6 of their papers we have counts for
6 papers
From Skin to Skeleton: Towards Biomechanically Accurate 3D Digital Humans
Marilyn Keller, Keenon Werling, Soyong Shin +4
Great progress has been made in estimating 3D human pose and shape from images and video by training neural networks to directly regress the parameters of parametric human models l…
AddBiomechanics Dataset: Capturing the Physics of Human Motion at Scale
Keenon Werling, Janelle Kaneda, Alan Tan +15
While reconstructing human poses in 3D from inexpensive sensors has advanced significantly in recent years, quantifying the dynamics of human motion, including the muscle-generated…
Real-time Model Predictive Control and System Identification Using Differentiable Physics Simulation
Sirui Chen, Keenon Werling, Albert Wu +1
Developing robot controllers in a simulated environment is advantageous but transferring the controllers to the target environment presents challenges, often referred to as the "si…
Fast and Feature-Complete Differentiable Physics for Articulated Rigid Bodies with Contact
Keenon Werling, Dalton Omens, Jeongseok Lee +2
We present a fast and feature-complete differentiable physics engine, Nimble (nimblephysics.org), that supports Lagrangian dynamics and hard contact constraints for articulated rig…
Robust Subgraph Generation Improves Abstract Meaning Representation Parsing
Keenon Werling, Gabor Angeli, Christopher Manning
The Abstract Meaning Representation (AMR) is a representation for open-domain rich semantics, with potential use in fields like event extraction and machine translation. Node gener…
On-the-Job Learning with Bayesian Decision Theory
Keenon Werling, Arun Chaganty, Percy Liang +1
Our goal is to deploy a high-accuracy system starting with zero training examples. We consider an "on-the-job" setting, where as inputs arrive, we use real-time crowdsourcing to re…