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

What this post added

This post details how a robotics researcher leveraged JAX for computational efficiency in optimal control and simulation. Key contributions include the use of JAX's vmap for parallelization and scan for trajectory simulation, achieving significant speedups. It also highlights the seamless integration of model-based and learning-based approaches using JAX's functional paradigm and automatic differentiation, exemplified by the development of the LQRax library for vectorized and differentiable LQR. The post also touches on JAX's growing support for embedded platforms like Jetson, crucial for robotics.