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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.
Author Information
Yizhou Zhao (UCLA)
Kaixiang Lin (Amazon)
Zhiwei Jia (University of California, San Diego)
Qiaozi Gao (Amazon)
Govindarajan Thattai (Amazon)
Jesse Thomason (University of Southern California)
Gaurav Sukhatme (USC)
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