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

- Source: arXiv cs.RO
- Published: 2026-10-27T04:00:00.000Z
- RoboHot: https://robohot.news/en/items/mtap654ie9b7gy3j5kyc6bzxa
- Original: https://arxiv.org/abs/2606.01478

## Summary

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.