🤖 AI Summary
This study addresses the bottleneck of months-long manual tuning required to translate digital designs into robotic assembly by proposing an end-to-end autonomous pipeline. Methodologically, it integrates generative AI with natural language processing to automate the design of customized timber structures. Furthermore, it introduces a novel gradient backpropagation mechanism via a graph attention network surrogate, enabling hardware-level corrections through differentiable geometry repair to drive collaborative UR5e robotic assembly. Experimental results demonstrate that 86.7% of novel inputs achieve automated screw driving, yielding a tenfold efficiency improvement over conventional methods. The proposed framework is further validated through the successful physical assembly of ten distinct structures.
📝 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.