DroneDiffusion: Robust Quadrotor Dynamics Learning with Diffusion Models

Avirup Das*¹, Rishabh Yadav*¹, Sihao Sun², Mingfei Sun¹, Samuel Kaski1 ³, Wei Pan¹

¹ Department of Computer Science, The University of Manchester

² Department of Cognitive Robotics, Delft University of Technology 

³ Department of Computer Science, Aalto University

* Equal Contribution

Experiment 1: Generalization to Unseen Complex Trajectory

Task: Training data are collected by manoering the quadrotor on the primitive trajectories and then evaluated on the complex flight paths unseen during training.

Experiment 2 (A): Adaption against Payload and Velocities

Task: Training data are collected by manoering the quadrotor with the payloads {0.2, 0.6} kg and velocities {0.2, 0.4} m/s.

Evaluation: (i) Payload {0.4} kg and velocity {0.35} m/s are in distribution. 

  (ii) Payload {0.8} kg and velocity {0.50} m/s are out of distribution.

Experiment 2 (B): Adaption against Wind

Task: Stabilizing the quadrotor at (0, 0, 0.8) m under external wind disturbance. 

Experiment 3: Runtime performance with multi-step predictions

Task: Stabilizing the quadrotor at (0, 0, 0.8) m for 60 sec for different horizon lengths. Sampling from the diffusion models are then used sequentially in the control law.

BibTeX

@article{das2024dronediffusion,

  title={DroneDiffusion: Robust Quadrotor Dynamics Learning with Diffusion Models},

  author={Das, Avirup and Yadav, Rishabh Dev and Sun, Sihao and Sun, Mingfei and Kaski, Samuel and Pan, Wei},

  journal={arXiv preprint arXiv:2409.11292},

  year={2024}

}