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...
| 主要な著者: | , , , , , , |
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| 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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