Learning object affordances is an effective tool in the field of robot learning. While the data-driven models delve into the exploration of affordances of single or paired objects, there is a notable gap in the investigation of affordances of compound objects that are composed of an arbitrary number of objects with complex shapes. In this study, we propose Multi-Object Graph Affordance Network (MOGAN) that models compound object affordances and predicts the effect of placing new objects on top of the existing compound. Given different tasks, such as building towers of specific heights or properties, we used a search based planning to find the sequence of stack actions with the objects of suitable affordances. We showed that our system was able to correctly model the affordances of very complex compound objects that include stacked spheres and cups, poles, and rings that enclose the poles. We demonstrated the applicability of our system in both simulated and real-world environments, comparing our systems with a baseline model to highlight its advantages.

翻译：学习对象功能是机器人学习领域的一种有效工具。尽管数据驱动模型深入探索了单个或成对对象的功能，但在由任意数量复杂形状对象组成的复合对象功能研究方面仍存在显著空白。在本研究中，我们提出了多目标图属性网络（MOGAN），该网络对复合对象功能进行建模，并预测在现有复合结构上放置新对象的效果。针对不同任务（例如建造特定高度或属性的塔），我们采用基于搜索的规划方法，利用具有适当功能的对象寻找堆叠动作序列。实验表明，我们的系统能够准确建模包含堆叠球体与杯体、杆件以及环绕杆件的圆环等极复杂复合对象的功能。我们分别在模拟环境和真实环境中验证了系统的适用性，并与基线模型进行了对比以凸显其优势。