Deluca -- A Differentiable Control Library: Environments, Methods, and Benchmarking

Alex Jiawen Yu
Anirudha Majumdar
Cyril Zhang
John Olof Hallman
Karan Singh
Paula Nicoleta Gradu
Udaya Ghai
Differentiable computer vision, graphics, and physics in machine learning workshop at Neurips 2020 (2020) (to appear)
Google Scholar

Abstract

We present an open-source library of natively differentiable physics and robotics environments, accompanied by gradient-based control methods and a benchmarking suite.
The introduced environments allow auto-differentiation through the simulation dynamics, and thereby permit fast training of controllers. The library features several popular environments, including classical control settings from OpenAI Gym \cite{brockman2016openai}. We also provide a novel differentiable environment, based on deep neural networks, that simulates medical ventilation.

We give several use-cases of new scientific results obtained using the library. This includes a medical ventilator simulator and controller, an adaptive control method for time-varying linear dynamical systems, and new gradient-based methods for control of linear dynamical systems with adversarial perturbations.

Research Areas