Omar Eldardeer

h-index2
2papers
16citations

2 Papers

6.0ROMay 12Code
Mapping Embodied Affective Touch Strategies on a Humanoid Robot

Qiaoqiao Ren, Omar Eldardeer, Francesca Cocchella et al.

Affective touch in human-robot interaction is shaped not only by emotional intent, but also by robot embodiment, including touch location, physical constraints, and perceived agency or social role. Existing HRI studies typically focus on one or two isolated body parts, limiting understanding of how affective touch generalises across the full humanoid body. We present a study with 32 participants interacting with the iCub robot, which is equipped with full-body distributed tactile sensors. Participants expressed eight emotions under three conditions: free touch, arm-only touch, and torso-only touch. Results show that body region and spatial constraints jointly shaped both touch location and dynamics. In free touch, participants preferred socially accessible upper-body regions, while less frequently touched areas showed stronger emotion-specific selectivity. Emotion-related variation was more evident in motion features for arm-only touch and pressure features for torso-only touch. Touch strategies also did not transfer directly between free and constrained conditions, even within the same coarse body region. Participants reported increased closeness to the robot after interaction, with around 30 percent reporting a change in perceived social relationship. Together, these findings show that affective touch expression is strongly body-region dependent and shaped by embodiment constraints.

3.6CVSep 28, 2025
Gaze Estimation for Human-Robot Interaction: Analysis Using the NICO Platform

Matej Palider, Omar Eldardeer, Viktor Kocur

This paper evaluates the current gaze estimation methods within an HRI context of a shared workspace scenario. We introduce a new, annotated dataset collected with the NICO robotic platform. We evaluate four state-of-the-art gaze estimation models. The evaluation shows that the angular errors are close to those reported on general-purpose benchmarks. However, when expressed in terms of distance in the shared workspace the best median error is 16.48 cm quantifying the practical limitations of current methods. We conclude by discussing these limitations and offering recommendations on how to best integrate gaze estimation as a modality in HRI systems.