AI 中文总结
针对现有互锁组件生成方法依赖手工启发式、难处理复杂案例的问题，研究人员提出强化学习框架RL-Lock，结合结构化动作分块与MCTS学习，高效生成互锁组件，在复杂场景表现更优。
AI 中文摘要
互锁组件是指仅通过部件几何排列实现连接、无需胶水或钉子等外部连接件的装配结构，因结构稳定性已广泛应用于各类实际场景。生成互锁组件通常被建模为形状分解问题，即将体素网格表示的目标3D物体划分为指定数量的互锁部件。研究人员发现，生成互锁组件本质是序列决策问题，智能体需反复决策每个体素应归属的部件。受此启发，研究人员提出首个用于生成互锁组件的强化学习框架RL-Lock，无需像现有工作那样依赖手工设计的搜索启发式。RL-Lock结合结构化动作分块与MCTS引导的策略-价值学习，可高效探索互锁组件生成的大型组合搜索空间。实验表明，RL-Lock能有效生成互锁组件，尤其在现有方法耗时过长或无法找到有效解的具有挑战性的案例中表现突出。
英文摘要
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.
