From 2f2981974600ce1ad6defa928400b260e0245d47 Mon Sep 17 00:00:00 2001 From: Tyler Ingebrand Date: Tue, 15 Oct 2024 09:59:24 -0500 Subject: [PATCH] video --- index.html | 10 +++++----- static/videos/mpc_trajectory_hd_with_text.mp4 | Bin 0 -> 1460451 bytes 2 files changed, 5 insertions(+), 5 deletions(-) create mode 100644 static/videos/mpc_trajectory_hd_with_text.mp4 diff --git a/index.html b/index.html index 7340b16..33fef13 100644 --- a/index.html +++ b/index.html @@ -131,12 +131,12 @@

Zero-Shot Transfer of Neural ODEs

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- Aliquam vitae elit ullamcorper tellus egestas pellentesque. Ut lacus tellus, maximus vel lectus at, placerat pretium mi. Maecenas dignissim tincidunt vestibulum. Sed consequat hendrerit nisl ut maximus. -

+ + +
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Abstract

- Lorem ipsum dolor sit amet, consectetur adipiscing elit. Proin ullamcorper tellus sed ante aliquam tempus. Etiam porttitor urna feugiat nibh elementum, et tempor dolor mattis. Donec accumsan enim augue, a vulputate nisi sodales sit amet. Proin bibendum ex eget mauris cursus euismod nec et nibh. Maecenas ac gravida ante, nec cursus dui. Vivamus purus nibh, placerat ac purus eget, sagittis vestibulum metus. Sed vestibulum bibendum lectus gravida commodo. Pellentesque auctor leo vitae sagittis suscipit. + Autonomous systems often encounter environments and scenarios beyond the scope of their training data, which underscores a critical challenge: the need to generalize and adapt to unseen scenarios in real time. This challenge necessitates new mathematical and algorithmic tools that enable adaptation and zero-shot transfer. To this end, we leverage the theory of function encoders, which enables zero-shot transfer by combining the flexibility of neural networks with the mathematical principles of Hilbert spaces. Using this theory, we first present a method for learning a space of dynamics spanned by a set of neural ODE basis functions. After training, the proposed approach can rapidly identify dynamics in the learned space using an efficient inner product calculation. Critically, this calculation requires no gradient calculations or retraining during the online phase. This method enables zero-shot transfer for autonomous systems at runtime and opens the door for a new class of adaptable control algorithms. We demonstrate state-of-the-art system modeling accuracy for two MuJoCo robot environments and show that the learned models can be used for more efficient MPC control of a quadrotor.

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