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).

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...

Journal HAR Deep Learning

IM HERE – Interaction Model for Human Effort based Robot Engagement

April 29, 2025

We present our novel engagement modeling framework IMHERE, designed to improve human-robot interaction by capturing relational dynamics using a formal...

ROBOVIS 2025 Conference

Automating 3D Dataset Generation with Neural Radiance Fields

April 29, 2025

We present a fully automated pipeline that leverages Radiance Field–based universal 3D representations to rapidly generate high-quality models of arbitrary...

ROBOVIS 2025 Conference

Toward Truly Intelligent Autonomous Systems A Taxonomy of LLM Integration for Everyday Automation

April 28, 2025

With the rapid development of large language models (LLMs), their integration into autonomous systems has become essential. This integration significantly...

ROBOVIS 2025 Conference