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：学习物体功能属性是机器人学习领域的一种有效工具。尽管数据驱动模型已深入探索单个或成对物体的功能属性，但对于由任意数量具有复杂形状的物体组成的复合物体的功能属性研究仍存在显著空白。在本研究中，我们提出了多物体图灵赋能力网络（MOGAN），该网络建模复合物体功能属性并预测在现有复合物体上放置新物体的效果。针对不同任务（例如搭建特定高度或属性的塔），我们采用基于搜索的规划方法，利用具有合适功能属性的物体寻找堆叠动作序列。实验表明，我们的系统能够正确建模包含堆叠球体、杯子、杆子以及环绕杆子的圆环等复杂复合物体的功能属性。我们在仿真环境和真实环境中均验证了系统的适用性，并将系统与基线模型进行对比以突出其优势。