Deluca -- A Differentiable Control Library: Environments, Methods, and Benchmarking
Paula Gradu, John Hallman, Daniel Suo, Alex Yu, Naman Agarwal, Udaya, Ghai, Karan Singh, Cyril Zhang, Anirudha Majumdar, Elad Hazan

TL;DR
DeLuca is an open-source library offering differentiable physics environments and gradient-based control methods, enabling fast controller training and supporting scientific research in robotics and medical simulations.
Contribution
It introduces a comprehensive differentiable control library with environments, methods, and benchmarking tools, including novel medical ventilation simulation and control techniques.
Findings
Medical ventilator simulation and control demonstrated.
New gradient-based control methods for dynamical systems.
Benchmarking suite facilitates scientific research.
Abstract
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsModel Reduction and Neural Networks · Reinforcement Learning in Robotics · Advanced Control Systems Optimization
