Speech to Reality
On-Demand Production using Natural Language, 3D Generative AI, and Discrete Robotic Assembly



Department of Architecture, Department of Electrical Engineering and Computer Science, Department of Mechanical Engineering
Center for Bits and Atoms
Massachusetts Institute of Technology

Under Review for IEEE International Conference for Robotics and Automation (ICRA 2025)



Abstract - We present a system that transforms speech into physical objects by combining 3D generative Artificial Intelligence with robotic assembly. The system leverages natural language input to make design and manufacturing more accessible, enabling individuals without expertise in 3D modeling or robotic programming to create physical objects. We propose utilizing discrete robotic assembly of lattice-based voxel components to address the challenges of using generative AI outputs in physical production, such as design variability, fabrication speed, structural integrity, and material waste. The system interprets speech to generate 3D objects, discretizes them into voxel components, computes an optimized assembly sequence, and generates a robotic toolpath. 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 6-axis robotic arm.


 

If you find this work relevant, feel free to cite it as follows:

bibtex
@misc{kyaw2024speechrealityondemandproduction,
      title={Speech to Reality: On-Demand Production using Natural Language, 3D Generative AI, and Discrete Robotic Assembly}, 
      author={Alexander Htet Kyaw and Se Hwan Jeon and Miana Smith and Neil Gershenfeld},
      year={2024},
      eprint={2409.18390},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2409.18390},
}

A. H. Kyaw, S. H. Jeon, M. Smith, and N. Gershenfeld, "Speech to Reality: On-Demand Production using Natural Language, 3D Generative AI, and Discrete Robotic Assembly," arXiv preprint arXiv:2409.18390, 2024. Available: https://arxiv.org/abs/2409.18390.