UWB Meets Crazyflow: Simulating Degraded Feedback at Scale for Aerial Robotics
In this work, we introduce Crazyflow, an accurate, differentiable simulator built on JAX.
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Key points
- By leveraging jit compilation via XLA, Crazyflow unifies physics and control into a single differentiable computation graph, enabling massive parallelization on accelerated hardware without sacrificing modeling accuracy.
- This architecture achieves order-of-magnitude speedups over existing baselines, capable of training deployable reinforcement learning agents in seconds.
- To highlight its highly modular design, we demonstrate how easily Crazyflow can be extended by integrating a complete, high-fidelity Ultra-Wideband (UWB) and Inertial Measurement Unit (IMU) simulation pipeline coupled with a full-state Extended Kalman Filter (EKF).
- By combining speed, accuracy, and extensibility, Crazyflow serves as a foundational tool for the next generation of aerial robotics research.
Sources (1)
- [1]UWB Meets Crazyflow: Simulating Degraded Feedback at Scale for Aerial RoboticsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 12:13 PM
In this work, we introduce Crazyflow, an accurate, differentiable simulator built on JAX.
By leveraging jit compilation via XLA, Crazyflow unifies physics and control into a single differentiable computation graph, enabling massive parallelization on accelerated hardware without sacrificing modeling accuracy.
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