arXiv subjects

Atahan Caldir

Publications and source records attributed to Atahan Caldir.

2 recordsLinked to original sources

arXiv2025

Participant Perceptions of a Robotic Coach Conducting Positive Psychology Exercises: A Qualitative Analysis

This paper presents a qualitative analysis of participants' perceptions of a robotic coach conducting Positive Psychology exercises, providing insights for the future design of robotic coaches. Participants (n = 20) took part in a single-session (avg. 31 +- 10 minutes) Human-Robot Interaction study in a laboratory setting. We created the design of the robotic coach, and its affective adaptation, based on user-centred design research and collaboration with a professional coach. We transcribed post-study participant interviews and conducted a Thematic Analysis. We discuss the results of that analysis, presenting aspects participants found particularly helpful (e.g., the robot asked the correct questions and helped them think of new positive things in their life), and what should be improved (e.g., the robot's utterance content should be more responsive). We found that participants had no clear preference for affective adaptation or no affective adaptation, which may be due to both positive and negative user perceptions being heightened in the case of adaptation. Based on our qualitative analysis, we highlight insights for the future design of robotic coaches, and areas for future investigation (e.g., examining how participants with different personality traits, or participants experiencing isolation, could benefit from an interaction with a robotic coach).

cs.HC↗

arXiv2022

Continual Learning for Affective Robotics: A Proof of Concept for Wellbeing

Sustaining real-world human-robot interactions requires robots to be sensitive to human behavioural idiosyncrasies and adapt their perception and behaviour models to cater to these individual preferences. For affective robots, this entails learning to adapt to individual affective behaviour to offer a personalised interaction experience to each individual. Continual Learning (CL) has been shown to enable real-time adaptation in agents, allowing them to learn with incrementally acquired data while preserving past knowledge. In this work, we present a novel framework for real-world application of CL for modelling personalised human-robot interactions using a CL-based affect perception mechanism. To evaluate the proposed framework, we undertake a proof-of-concept user study with 20 participants interacting with the Pepper robot using three variants of interaction behaviour: static and scripted, using affect-based adaptation without personalisation, and using affect-based adaptation with continual personalisation. Our results demonstrate a clear preference in the participants for CL-based continual personalisation with significant improvements observed in the robot's anthropomorphism, animacy and likeability ratings as well as the interactions being rated significantly higher for warmth and comfort as the robot is rated as significantly better at understanding how the participants feel.

cs.HC↗