Relational Neural Expectation Maximization

Anonymous

Nov 07, 2017 (modified: Nov 07, 2017) ICLR 2018 Conference Blind Submission readers: everyone Show Bibtex
  • Abstract: Common-sense physical reasoning is an essential ingredient for any intelligent agent operating in the real-world. For example, it can be used to simulate the environment, or to infer the state of parts of the world that are currently unobserved. In order to match real-world conditions this causal knowledge must be learned without access to supervised data. To solve this problem, we present a novel method that incorporates prior knowledge about the compositional nature of human perception to factor interactions between object-pairs and to learn them efficiently. It learns to discover objects and to model physical interactions between them from raw visual images in a purely unsupervised fashion. On videos of bouncing balls we show the superior modelling capabilities of our method compared to other unsupervised neural approaches, that do not incorporate such prior knowledge. We show its ability to handle occlusion and that it can extrapolate learned knowledge to environments with different numbers of objects.
  • TL;DR: We introduce a novel approach to common-sense physical reasoning that learns physical interactions between objects from raw visual images in a purely unsupervised fashion
  • Keywords: Common-sense Physical Reasoning, Intuitive Physics, Representation Learning, Model building

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