Posts tagged: Conference

Extended Abstract accepted at CHI 2026: Teaching Cobots What to Do by Watching an Expert

DELEGACT: Let the Robot Watch, Then Decide Who Does What

Our extended abstract "Learning to Delegate and Act with DELEGACT: Multimodal Language Models for Task-Level Human–Cobot Planning in Industrial Assembly" has been accepted at CHI 2026 in Barcelona. This is work by Bram Verstappen together with Dries Cardinaels, Danny Leen, and Raf Ramakers at the Digital Future Lab (UHasselt - Flanders Make).

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Presented at EURECA-PRO Education & Research Days: Teaching as Training

Teaching as Training: Incremental and Iterative AI Skill Development

We presented our contribution “Teaching as Training: Iterative and Incremental AI Skill Development” () at the EURECA-PRO Education & Research Days in Hasselt, held under the theme Glocalising Universities: A Shifting Horizon. This is joint work with Jolien Notermans (Department of Educational Development, Policy and Quality Assurance) and Sarah Doumen (Faculty of Sciences) at Hasselt University. More details on the publication page. The visual story is generated using StoryBookly.

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Paper accepted at ICLR 2026: DIVERSE: Disagreement-Inducing Vector Evolution for Rashomon Set Exploration

DIVERSE: Finding the Many Faces of AI Decision-Making

Our paper “DIVERSE: Disagreement-Inducing Vector Evolution for Rashomon Set Exploration” () has been accepted at ICLR 2026, one of the top venues for machine learning research. This is joint work with my PhD student Gilles Eerlings, Brent Zoomers, Jori Liesenborgs, and Gustavo Rovelo Ruiz at the Digital Future Lab. More details on the publication page.

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Paper accepted at CHI 2026: Helping Humans Control Robots on the Moon

Every Move You Make: Helping Operators See Where Their Robot Will Go

Our paper "Every Move You Make: Visualizing Near-Future Motion Under Delay for Telerobotics" () has been accepted at CHI 2026 in Barcelona — the premier conference for human-computer interaction research. This is joint work with my PhD student Dries Cardinaels, Raf Ramakers, Tom Veuskens, Thomas Pietrzak (Univ. Lille, Inria), and Gustavo Rovelo Ruiz at the Digital Future Lab (UHasselt - Flanders Make). More details on the publication page.

Paper page on driescardinaels.be

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Two contributions Accepted for ACM VRST 2024 - AR Pattern Guidance and VR Text Input Modalities

Paper and Poster Accepted for ACM VRST 2024: AR Pattern Guidance and VR Text Input Modalities

We are excited to announce that both our paper and poster have been conditionally accepted for presentation at the ACM Symposium on Virtual Reality Software and Technology (VRST) 2024, which will take place in Trier, Germany.

Paper: Evaluation of AR Pattern Guidance Methods for a Surface Cleaning Task

Our full paper titled “Evaluation of AR Pattern Guidance Methods for a Surface Cleaning Task.” has been conditionally accepted.

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Paper accepted for ISMAR 2024 - The Art of Timing in AR Guidance

Paper accepted for ISMAR 2024: The Art of Timing in AR Guidance

We are excited to announce that our paper titled “The Art of Timing: Effects of AR Guidance Timing on Speed Control” (with Jeroen Ceyssens, Bram van Deurzen, Gustavo Rovelo Ruiz and Fabian Di Fiore) has been accepted for presentation at the 2024 IEEE International Symposium on Mixed and Augmented Reality (ISMAR).

graphical abstract

Abstract

Augmented Reality (AR) holds significant potential to facilitate users in executing manual tasks. For effective support, however, we need to understand how showing movement instructions in AR affects how well people can follow those movements in real life. In this paper, we examine the degree to which users can synchronize the speed of their movements with speed cues presented through an AR environment. Specifically, we investigate the effects of timing in AR visual guidance. We assess performance using a highly realistic Mixed Reality (MR) welding simulation. Welding is a task that requires very precise timing and control over hand and arm motion. Our results show that upfront visual guidance (before manual task execution) alone often fails to transfer the knowledge of intended speeds, especially at higher target speeds. Live guidance during manual task execution provides more accurate speed results but typically requires a higher overshoot at the start. Optimal outcomes occur when visual guidance appears upfront and continues during the activity for users to follow through.

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