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arXiv · 1810.11177

Learning sparse relational transition models

Victoria Xia·Zi Wang·Leslie Pack Kaelbling
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Abstract

We present a representation for describing transition models in complex uncertain domains using relational rules. For any action, a rule selects a set of relevant objects and computes a distribution over properties of just those objects in the resulting state given their properties in the previous state. An iterative greedy algorithm is used to construct a set of deictic references that determine which objects are relevant in any given state. Feed-forward neural networks are used to learn the transition distribution on the relevant objects' properties. This strategy is demonstrated to be both more versatile and more sample efficient than learning a monolithic transition model in a simulated domain in which a robot pushes stacks of objects on a cluttered table.

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Victoria Xia, Zi Wang, Leslie Pack Kaelbling. 2018-10-26. Learning sparse relational transition models. https://arxiv.org/abs/1810.11177

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