Learning Object Permanence from Videos via Latent Imaginations

• URL: http://arxiv.org/abs/2310.10372v2
• Date: Thu, 11 Apr 2024 08:21:09 GMT
• Title: Learning Object Permanence from Videos via Latent Imaginations
• Abstract summary: We introduce a slot-based autoregressive deep learning system, the looped location and identity tracking model Loci-Looped. We show that Loci-Looped learns to adaptively fuse latent imaginations with pixel-space observations into consistent latent object-specific what and where encodings over time. Our work thus introduces the first self-supervised interpretable learning model that learns about object permanence directly from video data without supervision.
• Score: 4.293591000527724
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While human infants exhibit knowledge about object permanence from two months of age onwards, deep-learning approaches still largely fail to recognize objects' continued existence. We introduce a slot-based autoregressive deep learning system, the looped location and identity tracking model Loci-Looped, which learns to adaptively fuse latent imaginations with pixel-space observations into consistent latent object-specific what and where encodings over time. The novel loop empowers Loci-Looped to learn the physical concepts of object permanence, directional inertia, and object solidity through observation alone. As a result, Loci-Looped tracks objects through occlusions, anticipates their reappearance, and shows signs of surprise and internal revisions when observing implausible object behavior. Notably, Loci-Looped outperforms state-of-the-art baseline models in handling object occlusions and temporary sensory interruptions while exhibiting more compositional, interpretable internal activity patterns. Our work thus introduces the first self-supervised interpretable learning model that learns about object permanence directly from video data without supervision.

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