Learning affordances in object-centric generative models

Learning affordances in object-centric generative models

Given visual observations of a reaching task together with a stick-like tool, we propose a novel approach that learns to exploit task-relevant object affordances by combining generative modelling with a task-based performance predictor. The embedding learned by the generative model captures the fact...

書誌詳細
主要な著者:	Wu, Y, Kasewa, S, Groth, O, Salter, S, Sun, L, Parker Jones, O, Posner, H
フォーマット:	Conference item
言語:	English
出版事項:	International Conference on Machine Learning 2020

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