On-Demand Robotic Assembly via Differentiable Geometric Part Repair

arXiv:2610.09777 · cs.RO · Submitted 2026-10-07 · Read on arXiv

cs.RO

Submitted: 2026-10-07

Updated: 2026-10-07

Comments: 8 pages, 4 figures, and 2 tables

Code: https://github.com/ammar-n-abbas/FoundationPoseROS2

License: http://creativecommons.org/licenses/by/4.0/

The gist: Transitioning from a digital design to a robotic assembly process currently requires months of expert manual tuning to reconcile part geometries with robotic constraints.

Terminology

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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