Industrial assembly is shifting toward human-robot collaboration (HRC) to leverage the complementary strengths of both agents. However, traditional task allocation referred to as the Robotic Assembly Line Balancing Problem (RALBP) remains labor-intensive and often lacks transparency. We introduce DELEGACT, a framework designed to produce workable, intelligible human-cobot task allocations. The framework uses a Vision-Language Model (VLM) to extract atomic operations from expert demonstration videos, then employs a Large Language Model (LLM) to delegate these tasks based on robot specifications, operator competencies, and material definitions. We provide a proof-of-concept prototype and preliminary testing on illustrative cases. Results demonstrate the system's ability to reason about complex constraints such as precision, weight, and ergonomics. This paper illustrates how off-the-shelf foundation models can automate HRC decision-making via a human-in-the-loop paradigm while preserving operator agency and understanding.
Posts tagged: HCI
Every move you make: Visualizing near-future motion under delay for telerobotics
Delays in direct teleoperation decouple operator input from robot feedback. We frame this not as a unitary problem but as three facets of operator uncertainty: (1) communication, when commands take effect, (2) trajectory, how inputs map to motion, and (3) environmental, how external factors alter outcomes. We externalized each facet through predictive visualizations: Network, Path, and Envelope. In a controlled study with 24 participants (novices in telerobotics) navigating a simulated robot under a fixed 2.56s round-trip delay, we compared these visualizations against a delayed-video baseline. Path significantly shortened task time, lowered perceived cognitive load, and reduced reliance on reactive "move-and-wait" behavior. Envelope lowered cognitive load but did not significantly reduce reactive behavior or improve performance, while Network had no measurable effect. These results indicate that predictive support is effective only when trajectory uncertainty is externalized, enabling operators to move from reactive to more proactive control
Two student projects from the UHasselt Human-AI Interaction course featured in SAI Update
The SAI Update magazine (Nov 2025 , sia.be) selected two projects from our Human–AI Interaction (HAII) course for its Next Technology Generation special. Proud of our students Linsey Helsen and Xander Vervaecke who turned their Human-AI Interaction project ideas into concrete, useful systems.
1) A Multi-Agent Approach to Fact-Checking (, ) — Xander Vervaecke (UHasselt) Xander’s LieSpy.ai coordinates multiple LLMs (e.g., GPT, Gemini, Mistral) to verify claims, compare reasoning, and aggregate evidence into a transparent verdict. The interface exposes sources, trust scores, and model rationales, moving fact-checking beyond a single-model answer. Key ideas: multi-agent collaboration, cross-validation, explainability.
Student Project Results from Human-AI Interaction Course
I am pleased to share the results of individual student projects from our Human-AI Interaction (HAII) course at Hasselt University. The YouTube playlist showcases some of the work our students have done throughout the course.
Short paper on Delay-Invariant Telerobotic Interaction accepted for Intelligent User Interfaces 2025
Our short paper, Challenges and Opportunities for Delay-Invariant Telerobotic Interactions, has been accepted for the 29th ACM International Conference on Intelligent User Interfaces (IUI 2025).
Engineering interactive systems embedding AI technologies (3rd workshop on)
EICS 2025 foreword
Challenges and opportunities for delay-invariant telerobotic interactions (short paper)
Effective operation in direct-control telerobotics relies heavily on real-time communication between the operator and the robot, as the operator retains full control over the robot's actions. However, in scenarios involving long distances, communication delays disrupt this feedback loop, creating significant challenges for precise control. To investigate these challenges, we conducted a user study where participants operated a TurtleBot3 Waffle Pi under varying delay conditions. Post-experiment brainstorming and analysis revealed recurring challenges, including over-correction, unpredictable robot behavior, and reduced situational awareness. Potential solutions identified include improving robot behavior predictability, integrating feedforward mechanisms, and enhancing visual feedback. These findings underscore the importance of designing intelligent interfaces to mitigate the impact of delays on telerobotic performance.
Paper on A Visual Dashboard for Model Multiplicity
In AI research, model multiplicity can help users better understand the diversity of AI predictions. Our new system “AI-Spectra” provides a visual dashboard to harness this concept effectively. Instead of relying on a single AI model, AI-Spectra uses multiple models—each seen as an expert—to produce predictions for the same task. This helps users see not only what different models agree or disagree on, but also why these differences occur. Gilles Eerlings (a FAIR PhD student ) and Sebe Vanbrabant where the main contributors for this work and combined machine learning, model multiplicity and visualisations that focus on the characteristics of an AI model, instead of explaining the behaviour.
Paper on Anthropomorphic User Interfaces
Anthropomorphic User Interfaces
Together with Eva Geurts, we explored Anthropomorphic User Interfaces (AUIs) and created a taxonomy that helps us to analyze, identify, and design appropriate AUIs. The paper is available here, and our interactive tool that helps you to find related resources for specific aspects from our technology is available at this URL: https://anthropomorphic-ui.onrender.com.
Citation
@inproceedings{geurtsantropomorphic2024,
title = {Anthropomorphic User Interfaces: Past, Present and Future of
Anthropomorphic Aspects for Sustainable Digital Interface Design},
author = {Eva Geurts and Kris Luyten},
booktitle = {Proceedings of the European Conference on Cognitive Ergonomics 2024},
articleno = {31},
numpages = {7},
keywords = {Anthropomorphism, Human-like interfaces, Taxonomy, User interface design},
location = {Paris, France},
series = {ECCE '24},
year = {2024},
publisher = {Association for Computing Machinery},
url={https://anthropomorphic-ui.onrender.com},
doi = {10.1145/3673805.3673831},
isbn = {9798400718243}
}
Abstract
Interactions with computing systems and conversational services such as ChatGPT have become an inherent part of our daily lives. It is surprising that user interfaces, the gateways through which we communicate with an interactive intelligent system, are still predominantly devoid of hedonic aspects. There is little attempt to make communication through user interfaces intentionally more like communication with humans. Anthropomorphic user interfaces can transform interactions with intelligent software into more pleasant experiences by integrating human-like attributes. Anthropomorphic user interfaces expose human-like attributes that enable people to perceive, connect, and interact with the interfaces as social actors. This integration of human-like aspects not only enhances user experience but also holds the potential to make interfaces more sustainable, as they rely on familiar human interaction patterns, thus potentially reducing the learning curve and increasing user adoption rates. However, there is little consensus on how to build these anthropomorphic user interfaces. We conducted an extensive literature review on existing anthropomorphic user interfaces for software systems (past), in order to map and connect existing definitions and interpretations in an overarching taxonomy (present). The taxonomy is used to organize and structure examples of anthropomorphic user interfaces into an accessible collection. The taxonomy and an accompanying web tool provide designers with a reference framework for analyzing and dissecting existing anthropomorphic user interfaces, and for designing new anthropomorphic user interfaces (future).