Budget-Aware Sequential Brick Assembly with Efficient Constraint Satisfaction

• URL: http://arxiv.org/abs/2210.01021v2
• Date: Thu, 03 Oct 2024 23:25:24 GMT
• Title: Budget-Aware Sequential Brick Assembly with Efficient Constraint Satisfaction
• Authors: Seokjun Ahn, Jungtaek Kim, Minsu Cho, Jaesik Park,
• Abstract summary: We tackle the problem of sequential brick assembly with LEGO bricks to create 3D structures. In particular, the number of assemblable structures increases exponentially as the number of bricks used increases. We propose a new method to predict the scores of the next brick position by employing a U-shaped sparse 3D convolutional neural network.
• Score: 63.672314717599285
• License:
• Abstract: We tackle the problem of sequential brick assembly with LEGO bricks to create combinatorial 3D structures. This problem is challenging since this brick assembly task encompasses the characteristics of combinatorial optimization problems. In particular, the number of assemblable structures increases exponentially as the number of bricks used increases. To solve this problem, we propose a new method to predict the scores of the next brick position by employing a U-shaped sparse 3D convolutional neural network. Along with the 3D convolutional network, a one-initialized brick-sized convolution filter is used to efficiently validate assembly constraints between bricks without training itself. By the nature of this one-initialized convolution filter, we can readily consider several different brick types by benefiting from modern implementation of convolution operations. To generate a novel structure, we devise a sampling strategy to determine the next brick position considering the satisfaction of assembly constraints. Moreover, our method is designed for either budget-free or budget-aware scenario where a budget may confine the number of bricks and their types. We demonstrate that our method successfully generates a variety of brick structures and outperforms existing methods with Bayesian optimization, deep graph generative model, and reinforcement learning.

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