2024–2027
Project Duration
ERDF
EU Funding
4
Research Focus Areas
12
Publications
26
Citations
ERDF research project
ENABLING
Resilient Human-Robot Collaboration in Mixed-Skill Environments
ENABLING
ENABLING (Resilient Human-Robot Collaboration in Mixed-Skill Environments) addresses the problem area of developing AI methods to complement the skills of robots and humans. It thus enables research innovations in cross-sectional areas of IT and key enabling technologies and forms the basis for future applications in the lead markets. The challenges lie, firstly, at the interface between robotics and AI and, secondly, in the complexity of tasks in a mixed-skill environment and, thirdly, in resilient and responsible collaboration. These are to be achieved by developing the key technologies for 1. robust recording of the affective user state, 2. semantic environment analysis, 3. intention-based interpretation of user actions, 4. and research into generative models for recording complex behavior in mixed-skill environments.
The project is funded by the European Regional Development Fund (ERDF) under grant No. ZS/2023/12/182056 and is planned with a project duration of 4 years (2024 to 2027).
Breakthrough Innovations
01
AI-Based User Perception
Robust AI perception of users in mixed-skill scenarios with occlusion, dynamic environments, and divided attention.
02
Social Signal Interpretation
Distinguishing task-directed actions, social interactions, and incidental movements from user signals and affective states.
03
Semantic Environment Analysis
Recognising user actions in spatial context — enabling intentional, anticipatory systems aware of environment and intent.
04
Affective State Detection
Real-time detection of user affect via gestures, facial expressions, and vital signs for non-verbal feedback and alignment.
05
Generative Behaviour Models
Generative AI models unifying multi-modal prediction to build comprehensive situation models of complex user behaviour.
Latest News
Deep Learning-Based Gaze Estimation: A Review
March 25, 2026
A comprehensive review of gaze estimation methodologies, tracing the evolution from conventional model- and feature-based approaches to modern deep learning...
Automating Synthetic Dataset Generation for Image-based 3D Detection: A Literature Review
October 19, 2025
A comprehensive review of synthetic dataset generation approaches for 3D object detection, covering both traditional 3D modeling and neural image...
Automated 3D Dataset Generation for Arbitrary Objects
October 10, 2025
An end-to-end pipeline that automates all aspects of 3D dataset generation by leveraging Radiance Fields for high-quality 3D modeling and...
A Real-Time Digital Twin Framework for the TIAGo Service Robot
October 05, 2025
We present a real-time digital twin of the TIAGo service robot built in Unity, featuring authentic kinematic mirroring, real-time sensor...
MobGazeNet: Robust Gaze Estimation Mobile Network Based on Progressive Attention Mechanisms
May 09, 2025
An efficient and lightweight network for gaze estimation that leverages progressive attention mechanisms to emphasize crucial eye features while maintaining...
Multi-Head Attention-Based Framework with Residual Network for Human Action Recognition
May 06, 2025
We propose a novel HAR framework integrating residual networks, Bi-LSTM, and multi-head attention with a motion-based frame selection strategy. It...