[2407.09147] AI-Powered Immersive Assistance for Interactive Task Execution in Industrial Environments
Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Human-Computer Interaction

arXiv:2407.09147 (cs)
[Submitted on 12 Jul 2024]

Title:AI-Powered Immersive Assistance for Interactive Task Execution in Industrial Environments

Authors:Tomislav Duricic, Peter Müllner, Nicole Weidinger, Neven ElSayed, Dominik Kowald, Eduardo Veas
View PDF HTML (experimental)
Abstract:Many industrial sectors rely on well-trained employees that are able to operate complex machinery. In this work, we demonstrate an AI-powered immersive assistance system that supports users in performing complex tasks in industrial environments. Specifically, our system leverages a VR environment that resembles a juice mixer setup. This digital twin of a physical setup simulates complex industrial machinery used to mix preparations or liquids (e.g., similar to the pharmaceutical industry) and includes various containers, sensors, pumps, and flow controllers. This setup demonstrates our system's capabilities in a controlled environment while acting as a proof-of-concept for broader industrial applications. The core components of our multimodal AI assistant are a large language model and a speech-to-text model that process a video and audio recording of an expert performing the task in a VR environment. The video and speech input extracted from the expert's video enables it to provide step-by-step guidance to support users in executing complex tasks. This demonstration showcases the potential of our AI-powered assistant to reduce cognitive load, increase productivity, and enhance safety in industrial environments.
Comments: 3 pages, 2 figures, Demo Paper accepted at the 50th European Conference on Artificial Intelligence
Subjects: Human-Computer Interaction (cs.HC); Information Retrieval (cs.IR)
Cite as: arXiv:2407.09147 [cs.HC]
  (or arXiv:2407.09147v1 [cs.HC] for this version)
  https://doi.org/10.48550/arXiv.2407.09147

Submission history

From: Tomislav Duricic [view email]
[v1] Fri, 12 Jul 2024 10:30:45 UTC (12,504 KB)
Full-text links:

Access Paper:

license icon view license
Current browse context:
cs.HC
< prev   |   next >
new | recent | 2024-07
Change to browse by:
cs
cs.IR

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)