Combinatorial assembly uses standardized unit primitives to build objects that satisfy user specifications. Lego is a widely used platform for combinatorial assembly, in which people use unit primitives (ie Lego bricks) to build highly customizable 3D objects. This paper studies sequence planning for physical combinatorial assembly using Lego. Given the shape of the desired object, we want to find a sequence of actions for placing Lego bricks to build the target object. In particular, we aim to ensure the planned assembly sequence is physically executable. However, assembly sequence planning (ASP) for combinatorial assembly is particularly challenging due to its combinatorial nature, ie the vast number of possible combinations and complex constraints. To address the challenges, we employ deep reinforcement learning to learn a construction policy for placing unit primitives sequentially to build the desired object. Specifically, we design an online physics-aware action mask that efficiently filters out invalid actions and guides policy learning. In the end, we demonstrate that the proposed method successfully plans physically valid assembly sequences for constructing different Lego structures. The generated construction plan can be executed in real.

翻译：组合装配利用标准化的单元基元构建满足用户规格的物体。乐高是一种广泛使用的组合装配平台，人们使用单元基元（即乐高积木）来构建高度可定制的三维物体。本文研究使用乐高进行物理组合装配的序列规划。给定目标物体的形状，我们希望找到一个放置乐高积木以构建目标物体的动作序列。特别地，我们的目标是确保规划的装配序列在物理上是可执行的。然而，由于组合装配的组合性质（即，可能组合的庞大数量与复杂约束），其装配序列规划（ASP）尤其具有挑战性。为应对这些挑战，我们采用深度强化学习来学习一种构造策略，用于顺序放置单元基元以构建所需物体。具体而言，我们设计了一种在线物理感知动作掩码，它能高效过滤无效动作并指导策略学习。最终，我们证明了所提出的方法能成功规划用于构建不同乐高结构的物理有效装配序列。生成的构造计划可以在现实中执行。