Keenon Werling — accepted AI-conference papers

2023

Real-Time Model Predictive Control and System Identification Using Differentiable Simulation

Sirui Chen, Keenon Werling, Albert Wu, C. Karen Liu

RA-L 2023

Transferring a controller from a simulated environment to a physical system is regarded as a challenging problem in robotics. We present a method for continuous improvement of modeling and control <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">after

Cited by 10 Source ↗Scholar ↗

2021

Fast and Feature-Complete Differentiable Physics Engine for Articulated Rigid Bodies with Contact Constraints

Keenon Werling, Dalton Omens, Jeongseok Lee, Ioannis Exarchos, C. Karen Liu

RSS 2021poster

We present a fast and feature-complete differentiable physics engine; Nimble (

Cited by 146 Source ↗PDF ↗Scholar ↗

2015

On-the-Job Learning with Bayesian Decision Theory

Keenon Werling, Arun Tejasvi Chaganty, Percy Liang, Christopher D. Manning

NeurIPS 2015spotlight

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 resolve uncertainty where needed and output our prediction when confident. As the model improves over time, the reliance o…

Cited by 31 Source ↗PDF ↗code ↗Scholar ↗