SplatOverflow: Asynchronous Hardware Troubleshooting
Cornell Tech , Cornell University
,Abstract
As tools for designing and manufacturing hardware become more accessible, smaller producers can develop and distribute novel hardware. However, there aren't established tools to support end-user hardware troubleshooting or routine maintenance. As a result, technical support for hardware remains ad-hoc and challenging to scale. Inspired by software troubleshooting workflows like StackOverflow, we propose a workflow for asynchronous hardware troubleshooting: SplatOverflow. SplatOverflow creates a novel boundary object, the SplatOverflow scene, that users reference to communicate about hardware. The scene comprises a 3D Gaussian Splat 3D Gaussian Splatting [Kerbl et al. 2023] is a view-interpolation and rasterization technique that renders a 3D scene as a collection of Gaussian distributions. of the user's hardware registered onto the hardware's CAD model. The splat captures the current state of the hardware, and the registered CAD model acts as a referential anchor for troubleshooting instructions. With SplatOverflow, maintainers can directly address issues and author instructions in the user's workspace. The instructions define workflows that can easily be shared between users and recontextualized in new environments. In this paper, we describe the design of SplatOverflow, detail the workflows it enables, and illustrate its utility to different kinds of users. We also validate that non-experts can use SplatOverflow to troubleshoot common problems with a 3D printer in a user study.
Demonstrative Examples
We demonstrate SplatOverflow with a variety of hardware examples, including a pick-and-place machine, a 3D printer, and an open-source e-reader.
BibTex
@misc{kwatra2024splatoverflowasynchronoushardwaretroubleshooting,
title={SplatOverflow: Asynchronous Hardware Troubleshooting},
author={Amritansh Kwatra and Tobias Wienberg and Ilan Mandel and Ritik Batra
and Peter He and Francois Guimbretiere and Thijs Roumen},
year={2024},
eprint={2411.02332},
archivePrefix={arXiv},
primaryClass={cs.HC},
url={https://arxiv.org/abs/2411.02332},
}
Acknowledgements
We would like to thank the Digital Life Initiative at Cornell Tech for supporting this work through a doctoral fellowship. We would also like to thank Joey Castillo, Frank Bu and Stephen Hawes for taking part in preliminary discussions that helped motivate this work.
Contact
If you have questions about this work, contact Amritansh Kwatra at ak2244 at cornell dot edu.