用强化学习自动生成无需胶水的拼接零件，解决传统方法慢且易失败的问题。
RL-Lock: Reinforcement Learning for Generating Interlocking Assemblies
- 将拼接生成建模为序列决策问题，用强化学习自动规划零件分配
- 在复杂案例中显著提升成功率，相比旧方法更快更稳定
- 适合需要快速设计可拆卸结构的工程与制造领域
拼接装配是指仅通过几何形状相互锁定而无需外部连接件（如胶水、钉子）的组件组合，因其结构稳定性被广泛应用于各类实际场景。生成拼接装配通常被建模为形状分解问题，即把目标3D物体（以体素网格表示）分割成指定数量的互锁零件。我们观察到，该过程本质上是序列决策问题：智能体需反复决定每个体素应归属哪个零件。受此启发，我们提出首个不依赖人工搜索启发式规则的强化学习框架RL-Lock，用于生成拼接装配。RL-Lock结合结构化动作分块与基于蒙特卡洛树搜索（MCTS）的策略-价值学习，高效探索庞大的组合搜索空间。实验表明，RL-Lock能有效生成拼接装配，尤其在现有方法耗时过长或无法找到可行解的挑战性案例中表现优异。
原文摘要 · Abstract (English)
An interlocking assembly is an assembly in which component parts are connected purely through their geometric arrangement, without relying on external connectors such as glue and nails. Such assemblies have been widely used in a variety of real-world applications due to their structural stability. The problem of generating interlocking assemblies is generally formulated as a shape decomposition problem, where a target 3D object represented as a voxel grid is partitioned into a prescribed number of interlocking pieces. We observe that generating interlocking assemblies is inherently a sequential decision-making problem, where an agent repeatedly decides which piece each voxel should be assigned to. Inspired by the observation, we propose the first reinforcement learning framework RL-Lock for generating interlocking assemblies, without relying on handcrafted search heuristics as existing works did. RL-Lock combines structured action chunking with MCTS-guided policy-value learning to efficiently navigate the large combinatorial search space for interlocking assembly generation. We demonstrate through experiments that RL-Lock allows effective generation of interlocking assemblies, especially for challenging cases in which existing approaches take too long or even fail to find a valid solution.
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