3D objects (artifacts) are made to fulfill functions. Designing an object often starts with defining a list of functionalities or affordances (action possibilities) that it should provide, known as functional requirements. Today, designing 3D object models is still a slow and difficult activity, with few Computer-Aided Design (CAD) tools capable to explore the design solution space. The purpose of this study is to explore shape generation conditioned on desired affordances. We introduce an algorithm for generating voxelgrid object shapes which afford the desired functionalities. We follow the principle form follows function, and assume that object forms are related to affordances they provide (their functions). First, we use an artificial neural network to learn a function-to-form mapping from a dataset of affordance-labeled objects. Then, we combine forms providing one or more desired affordances, generating an object shape expected to provide all of them. Finally, we verify in simulation whether the generated object indeed possesses the desired affordances, by defining and executing affordance tests on it. Examples are provided using the affordances contain-ability, sit-ability, and support-ability.

制作3D对象（工件）以实现功能。设计对象通常始于定义其应提供的功能或能力（行动可能性）列表，称为功能要求。如今，设计3D对象模型仍然是一项缓慢而困难的工作，很少有计算机辅助设计（CAD）工具能够探索设计解决方案空间。这项研究的目的是探索以所需能力为条件的形状生成。我们介绍了一种生成能够提供所需功能的体素对象形状的算法。我们遵循原则形式跟随功能，并假定对象形式与它们提供的功能相关（它们的功能）。首先，我们使用人工神经网络从带有价格标签的对象的数据集中学习功能到形式的映射。然后，我们组合提供一个或多个所需报价的表单，生成预期提供所有这些报价的对象形状。最后，我们在仿真中通过定义并执行提供能力测试来验证生成的对象是否确实具有所需的能力。使用提供能力包含能力，就座能力和支持能力来提供示例。