DIGITAL FABRICATION

Reinforcement Learning for Table Generation

DIGITAL FABRICATION

Reinforcement Learning for Table Generation

Generative design has been explored in architecture to produce unprecedented geometries, however, in most cases, design constraints are limited during the design process. Limitations of existing generative design strategies include topological inconsistencies of the output geometries, dense design outputs, as the output format is often voxels or point clouds, and finally out-of-scope design constraints. In order to overcome such shortcomings, a novel reinforcement learning (RL) framework is explored in order to design a series of furniture with embedded design and fabrication constraints. (Collaborated with Mathias Bernhard, Reza Kakooee, Marirena Kladeftira, Benjamin Dillenburger)

強化学習を使ったデザイン手法の研究。デザインの制約条件を強化学習モデルに与えることで、実際に製作できるようにエージェントを訓練。