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Learning object affordances is an effective tool in the field of robotlearning. While the data-driven models delve into the exploration ofaffordances of single or paired objects, there is a notable gap in theinvestigation of affordances of compound objects that are composed of anarbitrary number of objects with complex shapes. In this study, we proposeMulti-Object Graph Affordance Network (MOGAN) that models compound objectaffordances and predicts the effect of placing new objects on top of theexisting compound. Given different tasks, such as building towers of specificheights or properties, we used a search based planning to find the sequence ofstack actions with the objects of suitable affordances. We showed that oursystem was able to correctly model the affordances of very complex compoundobjects that include stacked spheres and cups, poles, and rings that enclosethe poles. We demonstrated the applicability of our system in both simulatedand real-world environments, comparing our systems with a baseline model tohighlight its advantages.

Multi-Object Graph Affordance Network: Enabling Goal-Oriented Planning through Compound Object Affordances