DroneDiffusion: Robust Quadrotor Dynamics Learning with Diffusion Models

Understand

An inherent fragility of quadrotor systems stems from model inaccuracies and external disturbances.

• These factors hinder performance and compromise the stability of the system, making precise control challenging.
• Existing model-based approaches either make deterministic assumptions, utilize Gaussian-based representations of uncertainty, or rely on nominal models, all of which often fall short in capturing the complex, multimodal nature of real-world dynamics.
• This work introduces DroneDiffusion, a novel framework that leverages conditional diffusion models to learn quadrotor dynamics, formulated as a sequence generation task.