Séverin Lemaignan

Ancrer l’interaction : Gestion des connaissances pour la robotique interactive: Avec le développement de la robotique cognitive, le besoin d’outils avancés pour représenter, manipuler, raisonner sur les connaissances acquises par un robot a clairement été mis en avant. Mais stocker et manipuler des connaissances requiert tout d’abord d’éclaircir ce que l’on nomme connaissance pour un robot, et comment celle-ci peut-elle être représentée de manière intelligible pour une machine. Ce travail s’efforce dans un premier temps d’identifier de manière systématique les besoins en terme de représentation de connaissance des applications robotiques modernes, dans le contexte spécifique de la robotique de service et des interactions homme-robot. Nous proposons une typologie originale des caractéristiques souhaitables des systèmes de représentation des connaissances, appuyée sur un état de l’art détaillé des outils existants dans notre communauté. Dans un second temps, nous présentons en profondeur ORO, une instanciation particulière d’un système de représentation et manipulation des connaissances, conçu et implémenté durant la préparation de cette thèse. Nous détaillons le fonctionnement interne du système, ainsi que son intégration dans plusieurs architectures robotiques complètes. Un éclairage particulier est donné sur la modélisation de la prise de perspective dans le contexte de l’interaction, et de son interprétation en terme de théorie de l’esprit. La troisième partie de l’étude porte sur une application importante des systèmes de représentation des connaissances dans ce contexte de l’interaction homme-robot : le traitement du dialogue situé. Notre approche et les algorithmes qui amènent à l’ancrage interactif de la communication verbale non contrainte sont présentés, suivis de plusieurs expériences menées au Laboratoire d’Analyse et d’Architecture des Systèmes au CNRS à Toulouse, et au groupe Intelligent Autonomous System de l’université technique de Munich. Nous concluons cette thèse sur un certain nombre de considérations sur la viabilité et l’importance d’une gestion explicite des connaissances des agents, ainsi que par une réflexion sur les éléments encore manquant pour réaliser le programme d’une robotique “de niveau humain”

This document presents the outcomes of the Dagstuhl Seminar “Roadmap for Responsible Robotics,” held in September 2023 at the Leibniz Center for Informatics, Schloss Dagstuhl, Germany. The seminar brought together researchers from the fields of robotics, computer science, social and cognitive sciences, and philosophy with the aim of charting a path toward improving responsibility in robotic systems. Through intensive interdisciplinary discussions centered on the various values at stake as robotics increasingly integrates into human life, the participants identified key priorities to guide future research and regulatory efforts. The resulting road map outlines actionable steps to ensure that robotic systems coevolve with human societies, promoting human agency and humane values rather than undermining them. Designed for diverse stakeholders—researchers, policy makers, industry leaders, practitioners, nongovernmental organizations (NGOs), and civil society groups—this road map provides a foundation for collaborative efforts toward responsible robotics.

In social robots, visual attention expresses awareness of the scenario components and dynamics. As in humans, their attention should be driven by a combination of different attention mechanisms. In this article, we introduce multi-modal saliency maps, i.e., spatial representations of saliency that dynamically integrate multiple attention sources depending on the context. We provide the mathematical formulation of the model and an open source software implementation. Finally, we present an initial exploration of its potential in social interaction scenarios with humans and evaluate its implementation.

In the field of Human-Robot Interaction (HRI), a fundamental challenge is to facilitate human understanding of robots. The emerging domain of eXplainable HRI (XHRI) investigates methods to generate explanations and evaluate their impact on human-robot interactions. Previous works have highlighted the need to personalise the level of detail of these explanations to enhance usability and comprehension. Our paper presents a framework designed to update and retrieve user knowledge-memory models, allowing for adapting the explanations' level of detail while referencing previously acquired concepts. Three architectures based on our proposed framework that use Large Language Models (LLMs) are evaluated in two distinct scenarios: a hospital patrolling robot and a kitchen assistant robot. Experimental results demonstrate that a two-stage architecture, which first generates an explanation and then personalises it, is the framework architecture that effectively reduces the level of detail only when there is related user knowledge.

In Human-Robot Interaction research, assessing how humans understand the robots they interact with is crucial, particularly when studying the impact of explainability and transparency. Some studies evaluate objective understanding by analysing the accuracy of users' mental models, while others rely on perceived, self-reported levels of subjective understanding. We hypothesise that both dimensions of understanding may diverge, thus being complementary methods to assess the effects of explainability on users. In our study, we track the weekly progression of the users' understanding of an autonomous robot operating in a healthcare centre over five weeks. Our results reveal a notable mismatch between objective and subjective understanding. In areas where participants lacked sufficient information, the perception of understanding, i.e. subjective understanding, raised with increased contact with the system while their actual understanding, objective understanding, did not. We attribute these results to inaccurate mental models that persist due to limited feedback from the system. Future research should clarify how both objective and subjective dimensions of understanding can be influenced by explainability measures, and how these two dimensions of understanding affect other desiderata such as trust or usability.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Socially assistive robots offer promising opportunities to support older adults in healthcare settings by enhancing communication, reducing loneliness, and promoting emotional well-being. Beyond direct patient interaction, socially assistive robots may also assist healthcare professionals by facilitating information delivery, relieving workload, and improving the organization of care activities. However, ensuring that these systems are both acceptable and usable by older adults and healthcare staff remains a critical challenge, particularly in dynamic hospital environments. Objective: This study aims to evaluate the acceptability and usability of a social robot in a geriatric day care hospital among patients and their informal caregivers and to identify facilitators and barriers to the adoption of social robots in healthcare settings. Methods: Tests were conducted between May 2023 and July 2024, involving 97 participants, including patients (n=65) and informal caregivers (n=32), across three experiments, called waves. The ARI robot, developed by PAL Robotics, was used in this study. Participants were invited to interact spontaneously with the robot as long as they wished, after which they completed usability and acceptability standardized questionnaires. They were administered orally and complemented by open-comments. Results: Quantitative analysis showed a significant increase in usability and acceptability scores across the three experimental waves. and qualitative analysis revealed similar improvements and participants reported greater comfort and satisfaction, particularly after the integration of a large language model. Conclusions: Successive upgrades of the social assistive robot, driven by large-language-model integration, led to significant increases in acceptability and usability scores across the three experimental waves, both in patients and caregivers. Introducing the LLM enabled the robot to engage in dialogues that were more natural, coherent, and context-sensitive. These findings show the value of an iterative, user-centred refinement process when integrating social robots in geriatric care. Clinical Trial: The study was approved by the French national ethics committee: “Comité de Protection des Personnes, CPP Ouest II, Maison de la Recherche Clinique-CHU Angers” (IRB: 2021/20) and was compliant with the General Data Protection Regulation (GDPR) (DPO: 20210114153645, AP-HP register).

BACKGROUND Socially assistive robots (SARs) hold promise for supporting older adults (OAs) in hospital settings by promoting social engagement, reducing loneliness, and enhancing emotional well-being. They may also assist health care professionals by delivering information, managing routines, and alleviating workload. However, their acceptability and usability remain major challenges, particularly in dynamic real-world care environments. OBJECTIVE This study aimed to evaluate the acceptability and usability of a SAR in a geriatric day care hospital (DCH) and to identify key factors influencing its adoption by OAs and their informal caregivers. METHODS Over the course of 1 year, 97 participants (n=65, 67%, OA patients and n=32, 33%, informal caregivers) took part in a mixed methods evaluation of ARI, a socially assistive humanoid robot developed by PAL Robotics. ARI was deployed in the waiting area of a geriatric day care robot in Paris (France), where it interacted with users through voice-based dialogue. After each session, participants completed 2 standardized assessments, the Acceptability E-scale (AES) and the System Usability Scale (SUS), administered orally to ensure accessibility. Open-ended qualitative feedback was also collected to capture subjective experiences and contextual perceptions. RESULTS Acceptability scores significantly increased across waves (wave 1: mean 15.4/30, SD 5.81; wave 2: mean 20.9/30, SD 5.25; wave 3: mean 22.5/30, SD 4.23; P<.001). Usability scores also improved (wave 1: mean 47.9/100, SD 24.18; wave 2: mean 57.4/100, SD 22.46; wave 3: mean 69.3/100, SD 16.03; P<.001). A strong positive correlation was observed between acceptability and usability scores (r=0.664, P<.001). Qualitative findings indicated improved ease of use, clarity, and user satisfaction over time, particularly following the integration of a large language model (LLM) in wave 2, leading to more coherent, natural, and context-aware interactions. CONCLUSIONS Successive system enhancements, most notably the integration of an LLM, led to measurable gains in usability and acceptability among patients and informal caregivers. These findings underscore the importance of iterative, user-centered design in deploying SARs in geriatric care environments. CLINICALTRIAL Approved by the French national ethics committee (CPP Ouest II, IRB: 2021/20) as it did not involve randomization or clinical intervention

This tutorial aims at providing an up-to-date picture of the state-of-art regarding using the Robot Operating System (ROS) to build robots with socio-cognitive capabilities. The tutorial will briefly introduce the ROS4HRI framework, and show how it can be used to build a complete social robot architecture, from human perception to expressive social interaction. We will illustrate the full software integration required to implement an autonomous social robot using a combination of open-source ROS-based social perception modules, a semantic knowledge base, a Large Language Model (LLM), and multi-modal expressiveness. Participants will be able to follow along using a simple social interaction simulator, as well as their own webcams. The organisers will also provide a new PAL Robotics TIAGo Pro stand-alone head to demonstrate the same system running on actual hardware.