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arXiv · 2610.09777

On-Demand Robotic Assembly via Differentiable Geometric Part Repair

Millicent Schlafly·Fabio Schaub·Diogo Costa Pais·Luca Lelli·Janne Dvorak·Claire Colmont·Sven Marti·Mark D. Fuge
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Abstract

Transitioning from a digital design to a robotic assembly process currently requires months of expert manual tuning to reconcile part geometries with robotic constraints. This paper presents an end-to-end, autonomous pipeline for the design and physical construction of bespoke wooden assemblies. A generative AI agent translates user prompts into initial 3D geometries, balancing the visual fidelity of the design with select physical constraints. The assemblability of the design is further improved by a gradient-based repair stage that backpropagates through a graph attention network surrogate to adjust component geometries. In addition to correcting for disjointed and overlapping components, we demonstrate hardware-specific corrections, differentiably optimizing the geometry of components to enable robot screwdriving for 86.7% of 60 novel natural language inputs, significantly outperforming prior work by a factor of ten. For ten of the structures, we physically demonstrate assemblability with two UR5e robots. This work marks a meaningful step toward on-demand robotic manufacturing, enabling the rapid production of customized, low-volume goods.

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Millicent Schlafly, Fabio Schaub, Diogo Costa Pais, Luca Lelli, Janne Dvorak, Claire Colmont, Sven Marti, Mark D. Fuge. 2026-10-07. On-Demand Robotic Assembly via Differentiable Geometric Part Repair. https://arxiv.org/abs/2610.09777

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