用强化学习生成含旋转操作的CAD指令序列，提升复杂建模能力。
RLCAD: Reinforcement Learning Training Gym for Revolution Involved CAD Command Sequence Generation
- 基于CAD几何引擎构建强化学习训练环境，支持旋转等复杂操作
- 在B-Rep几何输入下生成序列，达到当前最优生成质量
- 适合需要自动化复杂三维建模的工业设计与AI研发人员
CAD命令序列是3D CAD系统中典型的参数化设计范式，通过叠加2D草图并执行拉伸、旋转和布尔运算等操作构建模型。尽管学术界对自动命令序列生成兴趣日增，但现有方法和数据集仅支持2D草图、拉伸和布尔运算。这一局限使得难以表示更复杂的几何形状。本文提出一个基于CAD几何引擎构建的强化学习（RL）训练环境（gym）。给定输入的边界表示（B-Rep）几何体，RL算法中的策略网络生成动作，该动作连同先前生成的动作在gym中处理，生成对应的CAD几何体，并反馈至策略网络。奖励由生成几何体与目标几何体之间的差异决定，用于更新RL网络。本方法支持超越草图、布尔和拉伸的操作，包括旋转操作。借助该训练环境，我们在从B-Rep几何体生成命令序列方面实现了当前最优（SOTA）质量。
原文摘要 · Abstract (English)
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.
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