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December 22, 2025Open Access

Prompt-to-Parts: Generative AI for Physical Assembly and Scalable Instructions

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Authors

DNDavid Noever

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Overview

The framework demonstrates modularity and fidelity in generating buildable outputs from natural language, suggesting new design options for manufacturing.

Key Points

  • This research aims to develop a framework for generating assembly instructions that bridge natural language and manufacturable outputs.
  • Framework uses a discrete parts vocabulary to generate instructions
  • Employs LDraw as a text-rich representation
  • Introduces a Python library for programmatic model generation
  • Evaluates outputs on complex prototypes like satellites and aircraft
  • Demonstrates modularity and fidelity in assembly instructions
  • Enables scalable design processes for over 3000 assembly parts
  • Proposes a novel connection between language and physical assembly through a 'bag of bricks' model

Cite This Study

David Noever (2025) studied this question.

synapsesocial.com/papers/69488bc877063b71e748ce9ahttps://doi.org/10.48550/arxiv.2512.15743
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Prompt-to-Product: Generative Assembly via Bimanual Manipulation2025
  2. 2Neural Assembler: Learning to Generate Fine-Grained Robotic Assembly Instructions from Multi-View Images2024
  3. 3Toward Automated Programming for Robotic Assembly Using ChatGPT2024
  4. 4Robotic assembly via self-prompt Segment Anything Model and discrete prompt optimization2026
  5. 5Translating intention: AI-assisted model generation for collaborative human–robot construction2026