Crazyflow: An Accurate, GPU-Accelerated, Differentiable Drone Simulator in JAX
Overview
Crazyflow is a new simulator for aerial robotics that combines high-fidelity dynamics with JAX for speed and differentiability. It supports both gradient-based and sampling-based methods, and enables in-flight reinforcement learning by training a recovery policy in under a second without domain randomization.
From arXiv Robotics — research abstracts
Event evidence and corrections
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Environment: simulation
“arXiv:2606.01478v3 Announce Type: replace Abstract: High-quality, large-scale synthetic data from simulations is becoming a cornerstone for pushing robots' capabilities. While aerial robotics simulators have evolved to support specialized needs such as fidelity, differentiability, and swarms independently, a unified platform that synthesizes data across all these domains is missing. In this work, we propose Crazyflow”
Exact source · revision 1Source owner not reported
Data origin: synthetic
“arXiv:2606.01478v3 Announce Type: replace Abstract: High-quality, large-scale synthetic data from simulations is becoming a cornerstone for pushing robots' capabilities. While aerial robotics simulators have evolved to support specialized needs such as fidelity, differentiability, and swarms independently, a unified platform that synthesizes data across all these domains is missing. In this work, we propose Crazyflow”
Exact source · revision 1Source owner not reported
Reported quantity · Reported duration: 0.37 other · Basis not reported
“g; we demonstrate this by throwing a physical drone into the air and training a recovery policy from scratch in 0.37 seconds, catching it before it hits the ground. By pushing accuracy, speed, and differentiability simultaneously, Crazyflow serves as an open-source resource for synthetic data generation, with emerging capabilities for large-scale parallelization for online, in-execution learning and optimization, ope”
Exact source · revision 1Source owner not reported
Evidence dependency changes (2)
- 2026-10-30T19:47:10.198Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-30T19:47:03.036Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
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- arXiv Robotics — research abstractsSignalCrazyflow: An Accurate, GPU-Accelerated, Differentiable Drone Simulator in JAX
Crazyflow is a new simulator for aerial robotics that combines high-fidelity dynamics with JAX for speed and differentiability. It supports both gradient-based and sampling-based methods, and enables in-flight reinforcement learning by training a recovery policy in under a second without domain randomization.
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