操作関係グラフ構築による関係指向アフォーダンス学習
Relationship Oriented Affordance Learning through Manipulation Graph Construction
シーン内の物体間の操作関係を表すManipulation Relationship Graphを提案し、それを画像から構築するAR-Netを開発した。関係認識と操作タスクでベースラインを上回る性能を示した。
著者: Chao Tang, Jingwen Yu, Weinan Chen, Hong Zhang
分類: cs.RO
原文アブストラクト
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)
