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 format used is often voxels or point clouds, and finally out-of-scope design constraints. In order to overcome such shortcomings, a novel reinforcement learning framework is explored in order to design a series of furniture with embedded design and fabrication constraints.

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Experiments demonstrate that the proposed method of reinforcement learning can generate a family of tables of unique aesthetics, satisfying topological consistency under design constraints. The model is successfully designed to fill a complicated geometry with a single stroke avoiding self-intersections as much as possible, meaning that the agent learns motion planning available for any geometry. In addition, a diverse range of visual expressions can be obtained by differentiating the geometric interpretations of the agent’s trail, e.g. piping along the trail or binary solidified voxels.