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)
@article{arxiv.2110.14137,
title = {Relationship Oriented Affordance Learning through Manipulation Graph Construction},
author = {Chao Tang and Jingwen Yu and Weinan Chen and Hong Zhang},
journal= {arXiv preprint arXiv:2110.14137},
year = {2021}
}