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################# | ||
Statement of Need | ||
################# | ||
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Simulation is an ever increasing data source for training deep learning models. | ||
In robotics, simulations have been successfully used to learn behaviors such as navigation, walking, flying or manipulation. | ||
The value of data generation in simulation mainly depends on the diversity and scale of scene layouts. | ||
Existing datasets :cite:`Mo_2019_CVPR,robotix2019,ehsani2021manipulathor,robocasa2024` are limited in that regard, whereas purely generative models still lack the ability to create scenes that can be used in physics simulator :cite:`schult24controlroom3d,yang2024physcenephysicallyinteractable3d,hoellein2023text2room`. | ||
Other procedural pipelines either focus on learning visual models :cite:`Denninger2023,greff2021kubric,infinigen2023infinite`, address specific use-cases such as autonomous driving :cite:`scenic2020,hess2021procedural`, or make it hard to be extended and customized since they are tightly integrated with a particular simulation platform :cite:`procthor`. | ||
With `scene_synthesizer` we present a library that simplifies the process of writing scene randomizers in Python, with a particular focus on physics simulations for robot manipulation. It is fully simulator-agnostic. | ||
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.. bibliography:: |
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