A roboticist's journey with JAX: Finding efficiency in optimal control and simulation- Google Developers Blog

This post details how a robotics researcher is leveraging JAX for computational efficiency in optimal control and simulation. It highlights the use of JAX's `vmap` for parallelization and `scan` for trajectory simulation, achieving significant speedups. The post also discusses integrating model-based control with learning-based representations using JAX's composability, automatic differentiation (`grad`), and functional paradigm. The researcher developed `LQRax`, a JAX-native LQR solver supporting GPU acceleration, `vmap`, `scan`, and `grad`, which complements existing JAX robotics tools like Brax and MJX.