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From Simulation to Reality With Random NoiseThe challenging environment of autonomous vehicle (AV) navigation necessitates certain functions be performed by deep neural networks. Optimizing these models involves collecting vast quantities of domain-specific training data and ensuring that the dataset is representative of expected conditions. High-fidelity simulation plays a vital role in making this process feasible, allowing a wide range of scenarios to be explored at low cost. However, learning from simulation introduces subtle biases into models, which can degrade real-world performance in unpredictable ways. This effect can be mitigated with learning schemes specialized to bridge distributional shifts (transfer learning). Given the complex nature of these methods, the underlying models, and their environments, meaningfully evaluating performance is notstraight forward. Many unrelated factors can effect an improvement in generalization accuracy, but a full ablation analysis is often difficult. To tease out signal from noise, it is necessary to understand how transfer learning performance is affected by noise itself. The goals of this paper are (i) to establish a domain randomization baseline for a simple classification transfer learning task and (ii) to validate the RRAV testbed as a platform for further research in sim-to-real learning. We generate imagery from a simulation of NASA Ames Research Center and train a small convolutional neural network (ConvNet) to classify position relative to a centerline. Further models are trained with different types of noise progressively added to the data. The models are deployed aboard the on-site test vehicle to test real-world performance. In our experiments, we find that such naive domain randomization raises sim-to-real accuracy from 64% to 79%, while training directly on real data yields an 89% accuracy ceiling. These results suggest that the isolated mechanism of domain randomization can significantly improve generalization.
Document ID
20250000242
Acquisition Source
Ames Research Center
Document Type
Presentation
Authors
Steven Luo
(University of California, Berkeley Berkeley, United States)
Rory Lipkis
(Ames Research Center Mountain View, United States)
Ignacio Lopez-Francos
(KBR (United States) Houston, Texas, United States)
Pavlo Vlastos
(KBR (United States) Houston, Texas, United States)
Adrian Agogino
(Ames Research Center Mountain View, United States)
Date Acquired
January 8, 2025
Subject Category
Cybernetics, Artificial Intelligence and Robotics
Meeting Information
Meeting: AIAA SciTech Forum
Location: Orlando, FL
Country: US
Start Date: January 6, 2025
End Date: January 10, 2025
Sponsors: American Institute of Aeronautics and Astronautics
Funding Number(s)
WBS: 340428.02.60.01.01
Distribution Limits
Public
Copyright
Portions of document may include copyright protected material.
Technical Review
NASA Peer Committee
Keywords
simulation
data augmentation
transfer learning
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