Speech to Reality: On-Demand Production using Natural Language, 3D Generative AI, and Discrete Robotic Assembly
Name
3745778.3766670.pdf
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14.63 MB
Format
Adobe PDF
Checksum (MD5)
428f92d314c05edf518dc3f974aa0822
Author(s)
Kyaw, Alexander Htet
Smith, Miana
Jeon, Se Hwan
Gershenfeld, Neil
Date Issued
November 19, 2025
Publisher
ACM|ACM Symposium on Computational Fabrication
Citation
Alexander Htet Kyaw, Miana Smith, Se Hwan Jeon, and Neil Gershenfeld. 2025. Speech to Reality: On-Demand Production using Natural Language, 3D Generative AI, and Discrete Robotic Assembly. In Proceedings of the ACM Symposium on Computational Fabrication (SCF '25). Association for Computing Machinery, New York, NY, USA, Article 16, 1–12.
Version
Final published version
Abstract
We present a system that transforms speech into physical objects using 3D generative AI and discrete robotic assembly. By leveraging natural language, the system makes design and manufacturing more accessible to people without expertise in 3D modeling or robotic programming. While generative AI models can produce a wide range of 3D meshes, AI-generated meshes are not directly suitable for robotic assembly or account for fabrication constraints. To address this, we contribute a workflow that integrates natural language, 3D generative AI, geometric processing, and discrete robotic assembly. The system discretizes the AI-generated geometry and modifies it to meet fabrication constraints such as component count, overhangs, and connectivity to ensure feasible physical assembly. The results are demonstrated through the assembly of various objects, ranging from chairs to shelves, which are prompted via speech and realized within 5 minutes using a robotic arm.
Description
SCF ’25, Cambridge, MA, USA
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3745778.3766670
