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arXiv · 1812.07768

Modular meta-learning in abstract graph networks for combinatorial generalization

Ferran Alet·Maria Bauza·Alberto Rodriguez·Tomas Lozano-Perez·Leslie P. Kaelbling
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Abstract

Modular meta-learning is a new framework that generalizes to unseen datasets by combining a small set of neural modules in different ways. In this work we propose abstract graph networks: using graphs as abstractions of a system's subparts without a fixed assignment of nodes to system subparts, for which we would need supervision. We combine this idea with modular meta-learning to get a flexible framework with combinatorial generalization to new tasks built in. We then use it to model the pushing of arbitrarily shaped objects from little or no training data.

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Ferran Alet, Maria Bauza, Alberto Rodriguez, Tomas Lozano-Perez, Leslie P. Kaelbling. 2018-12-19. Modular meta-learning in abstract graph networks for combinatorial generalization. https://arxiv.org/abs/1812.07768

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