  We present an open-source library of natively differentiable physics and
robotics environments, accompanied by gradient-based control methods and a
benchmark-ing 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. 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.

Robotics;Machine Learning