Relationship Oriented Affordance Learning through Manipulation Graph Construction

Understand

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.