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tyler-ingebrand committed Oct 15, 2024
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<meta name="viewport" content="width=device-width, initial-scale=1">


<title>Academic Project Page</title>
<title>Zero-Shot Transfer of Neural ODEs</title>
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We apply this algorithm to the MPC control of a quadrotor. The quadrotor's mass is varied every episode, and the goal is to reach prespecified coordinates.
Our results demonstrate that our approach makes more accurate long horizon predictions in the presence
of hidden parameters than neural ODEs alone. When measuring the slew rate of the controller, i.e. its efficiency, we observe that this improved prediction
accuracy leads to more efficient control of the quadrotor.
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Qualitatively, this difference is visible in the trajectories of the respective controllers. Here we are plotting one trajectory from each controller
for the same conditions. We observe that the neural ODE controller alone makes repeated corrections to its trajectory. In contrast, our approach more smoothly
approaches the target coordinates.
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<pre><code>BibTex Code Here</code></pre>
<pre><code>@misc{functionEncoderNeuralODEs2024,
title={Zero-Shot Transfer of Neural ODEs},
author={Tyler Ingebrand and Adam J. Thorpe and Ufuk Topcu},
year={2024},
journal={NeurIPS}.
}
</code></pre>
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</section>
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