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arXiv · 2110.14137

Relationship Oriented Affordance Learning through Manipulation Graph Construction

Chao Tang·Jingwen Yu·Weinan Chen·Hong Zhang
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Abstract

In this paper, we propose Manipulation Relationship Graph (MRG), a novel affordance representation which captures the underlying manipulation relationships of an arbitrary scene. To construct such a graph from raw visual observations, a deep nerual network named AR-Net is introduced. It consists of an Attribute module and a Context module, which guide the relationship learning at object and subgraph level respectively. We quantitatively validate our method on a novel manipulation relationship dataset named SMRD. To evaluate the performance of the proposed model and representation, both visual perception and physical manipulation experiments are conducted. Overall, AR-Net along with MRG outperforms all baselines, achieving the success rate of 88.89% on task relationship recognition (TRR) and 73.33% on task completion (TC)

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Chao Tang, Jingwen Yu, Weinan Chen, Hong Zhang. 2021-10-27. Relationship Oriented Affordance Learning through Manipulation Graph Construction. https://arxiv.org/abs/2110.14137

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