Article

Crazyflow: A JAX-based GPU-accelerated differentiable drone simulator

The authors propose Crazyflow, a differentiable drone simulator built on JAX that combines high-fidelity dynamics with GPU acceleration, scaling to millions of environments or swarms of thousands of drones on a single CPU and GPU. Real-world experiments show that it supports analytical-gradient policy learning, achieving high-accuracy tracking without domain randomization, and sampling-based obstacle avoidance at over 500 million steps per second. The authors also demonstrate in-flight reinforcement learning: after tossing a physical drone into the air, training a recovery policy from scratch took only 0.37 seconds, and the drone was caught before landing.

Part of the event: Crazyflow: A GPU-Accelerated Differentiable Drone Simulator

This page provides a summary and source link only.