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-

[Project Title]

-

[A concise tagline or summary of your research]

+

SOUS VIDE

+

Scene Optimized Understanding via Synthesized Visual Inertial Data from Experts

+
+ Sous Vide Pipeline + +
+

Demonstration

+

Watch the videos below for a demonstration of our project in action:

+
+ +
+
+ +
-

About the Project

-

This project introduces [describe your research briefly], offering a novel approach to [key research problem]. The goal is to [state primary objective or significance].

-

The paper demonstrates significant advancements in [specific domain, e.g., autonomous drone navigation, neural network interpretability, etc.], providing practical insights for researchers and practitioners alike.

+

Abstract

+

We propose a new simulator, training approach, and + policy architecture, collectively called SOUS VIDE, for end-to- + end visual drone navigation. Our trained policies exhibit zero- + shot sim-to-real transfer with robust real-world performance + using only on-board perception and computation. Our simulator, + called FiGS, couples a computationally simple drone dynamics + model with a high visual fidelity Gaussian Splatting scene re- + construction. FiGS can quickly simulate drone flights producing + photo-realistic images at over 100 fps. We use FiGS to collect + 100k-300k observation-action pairs from an expert MPC with + privileged state and dynamics information, randomized over + dynamics parameters and spatial disturbances. We then distill + this expert MPC into an end-to-end visuomotor policy with a + lightweight neural architecture, called SV-Net. SV-Net processes + color image and IMU data streams into low-level body rate and + thrust commands at 20Hz onboard a drone. Crucially, SV-Net + includes a Rapid Motor Adaptation (RMA) module that adapts + at runtime to variations in the dynamics parameters of the drone. + In extensive hardware experiments, we show SOUS VIDE polices + to be robust to ±30% mass and thrust variations, 40 m/s wind + gusts, 60% changes in ambient brightness, shifting or removing + objects from the scene, and people moving aggressively through + the drone’s visual field. The project page and code can be found

Key Contributions

@@ -113,12 +178,12 @@

Publication

Acknowledgments

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We extend our gratitude to [individuals, institutions, or funding agencies].

+

This work was supported in part by DARPA grant HR001120C0107, ONR grant N00014-23-1-2354, and Lincoln Labs grant 7000603941. The second author was supported on an NDSEG fellowhsip. Toyota Research Institute provided funds to support this work.

License

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This project is licensed under the [License Name] License. See the LICENSE file for more details.

+

This project is licensed under the GNU General Public License v3.0. See the LICENSE file for more details.