A CAD command sequence is a typical parametric design paradigm in 3D CAD systems where a model is constructed by overlaying 2D sketches with operations such as extrusion, revolution, and Boolean operations. Although there is growing academic interest in the automatic generation of command sequences, existing methods and datasets only support operations such as 2D sketching, extrusion,and Boolean operations. This limitation makes it challenging to represent more complex geometries. In this paper, we present a reinforcement learning (RL) training environment (gym) built on a CAD geometric engine. Given an input boundary representation (B-Rep) geometry, the policy network in the RL algorithm generates an action. This action, along with previously generated actions, is processed within the gym to produce the corresponding CAD geometry, which is then fed back into the policy network. The rewards, determined by the difference between the generated and target geometries within the gym, are used to update the RL network. Our method supports operations beyond sketches, Boolean, and extrusion, including revolution operations. With this training gym, we achieve state-of-the-art (SOTA) quality in generating command sequences from B-Rep geometries.

翻译：CAD指令序列是三维CAD系统中典型的参数化设计范式，通过叠加二维草图并辅以拉伸、旋转及布尔运算等操作构建模型。尽管学术界对指令序列自动生成的兴趣日益增长，现有方法与数据集仅支持二维草图绘制、拉伸和布尔运算等操作。这一局限使得表达更复杂的几何结构面临挑战。本文提出了一种基于CAD几何引擎构建的强化学习训练环境。给定输入的边界表示几何体，强化学习算法中的策略网络生成动作指令，该指令与先前生成的动作序列在训练环境中被处理，生成相应的CAD几何体并反馈至策略网络。通过计算生成几何体与目标几何体在环境中的差异来确定奖励值，用于更新强化学习网络。本方法支持草图绘制、布尔运算及拉伸之外的操作，包括旋转操作。借助此训练环境，我们在从边界表示几何体生成指令序列的任务中实现了当前最优的质量。