Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: LUMINOUS: Indoor Scene Generation for Embodied AI Challenges   Learning-based methods for training embodied agents typically require a large
number of high-quality scenes that contain realistic layouts and support
meaningful interactions. However, current simulators for Embodied AI (EAI)
challenges only provide simulated indoor scenes with a limited number of
layouts. This paper presents Luminous, the first research framework that
employs state-of-the-art indoor scene synthesis algorithms to generate
large-scale simulated scenes for Embodied AI challenges. Further, we
automatically and quantitatively evaluate the quality of generated indoor
scenes via their ability to support complex household tasks. Luminous
incorporates a novel scene generation algorithm (Constrained Stochastic Scene
Generation (CSSG)), which achieves competitive performance with human-designed
scenes. Within Luminous, the EAI task executor, task instruction generation
module, and video rendering toolkit can collectively generate a massive
multimodal dataset of new scenes for the training and evaluation of Embodied AI
agents. Extensive experimental results demonstrate the effectiveness of the
data generated by Luminous, enabling the comprehensive assessment of embodied
agents on generalization and robustness.

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The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
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The categories of this paper are: dg-ga
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The categories of this paper are: q-fin
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The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
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The categories of this paper are: stat
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The categories of this paper are: acc-phys
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The categories of this paper are: patt-sol
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The categories of this paper are: bayes-an
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The categories of this paper are: cmp-lg