AI4WORK Project
Project description
In a rapidly evolving technological landscape, the collaboration between humans and machines poses a pressing challenge to the modern workforce. As artificial intelligence (AI) and robotics become integral to various industries, striking the right balance between human ingenuity and machine efficiency remains elusive. The need for optimal work-sharing methods becomes paramount, spanning from manual labour to intricate decision-making processes. In this context, the EU-funded AI4Work project aims to explore and implement practical solutions for the seamless collaboration between humans and AI/robots. The key challenge lies in developing versatile tools like the sliding work sharing (SWS) approach, adapting the balance between human and machine activities based on situational context and interactions.
Project outputs
Publications
| Domain |
Type of output |
Title |
DOI URL
|
| AI, Machine Learning & Data Science |
Conference proceedings |
Manufacturing workers fatigue: an exploratory study on predictive machine learning and cross-subject generalization with implications for work design |
https://doi.org/10.1016/J.IFACOL.2024.09.271
|
| AI, Machine Learning & Data Science |
Conference proceedings |
Exploration of core concepts required for mid- and domain-level ontology development to facilitate explainable-AI-readiness of data and models |
https://doi.org/10.5281/ZENODO.13148630
|
| AI, Machine Learning & Data Science |
Peer reviewed articles |
AI4WORK Project: Human Centric Digital Twin Approaches to Trustworthy AI and Robotics for Improved Working Conditions in Healthcare and Education Sectors |
https://doi.org/10.3233/SHTI240581
|
| Healthcare, Medicine & Accessibility |
Peer reviewed articles |
Students' Burnout Symptoms Detection Using Smartwatch Wearable Devices: A Systematic Literature Review |
https://doi.org/10.3390/AISENS1010002
|
| Healthcare, Medicine & Accessibility |
Peer reviewed articles |
An Overview of Tools and Technologies for Anxiety and Depression Management Using AI |
https://doi.org/10.3390/APP14199068
|
| Robotics, Manufacturing & Industry 4.0 |
Book chapters |
A Meta-Engine Framework for Interleaved Task and Motion Planning using Topological Refinements |
https://doi.org/10.48550/ARXIV.2408.05795
|
| Robotics, Manufacturing & Industry 4.0 |
Conference proceedings |
Adaptive Human-Robot Collaborative Missions using Hybrid Task Planning |
https://doi.org/10.5281/ZENODO.17074178
|
| Robotics, Manufacturing & Industry 4.0 |
Conference proceedings |
Temporal Task and Motion Planning with Metric Time for Multiple Object Navigation |
https://doi.org/10.1609/AAAI.V39I25.34874
|
Technological assets
| Title |
Type of Asset |
Link / DOI |
Description
|
| Core concepts for mid- and domain-level ontology development |
Ontology / Framework |
https://doi.org/10.5281/ZENODO.13148630 |
Ontologies required to facilitate explainable-AI-readiness of data and models.
|
