Improving Generative Imagination in Object-Centric World Models

• URL: http://arxiv.org/abs/2010.02054v1
• Date: Mon, 5 Oct 2020 14:43:13 GMT
• Title: Improving Generative Imagination in Object-Centric World Models
• Authors: Zhixuan Lin, Yi-Fu Wu, Skand Peri, Bofeng Fu, Jindong Jiang, Sungjin Ahn
• Abstract summary: We introduce Generative Structured World Models (G-SWM) G-SWM unifies the key properties of previous models in a principled framework. It achieves two crucial new abilities, multimodal uncertainty and situation-awareness.
• Score: 20.495475118576604
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The remarkable recent advances in object-centric generative world models raise a few questions. First, while many of the recent achievements are indispensable for making a general and versatile world model, it is quite unclear how these ingredients can be integrated into a unified framework. Second, despite using generative objectives, abilities for object detection and tracking are mainly investigated, leaving the crucial ability of temporal imagination largely under question. Third, a few key abilities for more faithful temporal imagination such as multimodal uncertainty and situation-awareness are missing. In this paper, we introduce Generative Structured World Models (G-SWM). The G-SWM achieves the versatile world modeling not only by unifying the key properties of previous models in a principled framework but also by achieving two crucial new abilities, multimodal uncertainty and situation-awareness. Our thorough investigation on the temporal generation ability in comparison to the previous models demonstrates that G-SWM achieves the versatility with the best or comparable performance for all experiment settings including a few complex settings that have not been tested before.

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