Transparent and consistent reporting of user studies is essential for advancing scientific knowledge. In human-robot interaction (HRI), studies are often reported incompletely, even in top-tier venues, limiting proper evaluation, replication, and practical application of findings in practice. This study aimed to generate expert consensus on a reporting checklist for HRI user studies and provide a validated tool to improve transparency, reproducibility, and methodological rigor in the field, leading to easier translation of research into practice. A two-round Delphi study was conducted with 34 HRI experts from academia and industry from over 12 countries. An international panel of nine interdisciplinary experts first synthesized a preliminary list of 116 reporting items from the literature. Experts rated the importance of each item and provided qualitative feed- back. Consensus was defined as 70% agreement, and items were iteratively refined through anonymous online surveys. Overall, consensus was achieved on 106 items, encompassing both essential and context-dependent elements in nine domains. The resulting RUSH checklist (Reporting User Studies in Human-Robot Inter- action) provides the first community-endorsed, consensus-based reporting guideline for HRI user studies.

We introduce an open-source ROS 2 architecture for situated social robots, along with a simulator that allows mixed-reality development and interactions. The architecture is a hybrid symbolic/subsymbolic system that integrates explicit ontology semantics for perception, reasoning, and execution, with LLMs. It features multimodal social perception by leveraging the open source ROS4HRI framework; LLMs (both edge- and cloud-based) to facilitate natural language interaction between the user and system; KnowledgeCore, an open-source knowledge base, to reason about facts in the world; and an intent-based controller to supervise the execution of parallel/sequential tasks and skills. We demonstrate our system architecture with a social robot running the mixed-reality system.

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.

This paper presents the TIAGo Head, a new tabletop social robot from PAL Robotics, focusing on its capabilities as an HRI platform. We detail the robots’ hardware, highlighting its sensors/actuators and on-board computing; and its software architecture, including social perception, expressive face, a knowledge base, and integration with large language models (LLMs) for natural conversations. We also describe a use-case in a receptionist scenario where TIAGo Head dynamically interacts with travelers by displaying news and conversing.

Social robots for children have focused mainly on conventional education domains such as teaching language, science, and math, while applications focusing on the enhancement of cultural competency are quite scarce. In this paper, we present a prototype of a robot-mediation framework for cross-cultural communication. This framework paves the way for a social robot to act as a mediator between groups of schoolchildren from different countries. First, we conducted a participatory design activity by an interdisciplinary team, resulting in the extraction of the design, robot’s roles, and technical requirements. Based on these requirements, we built the robot-mediation system prototype. We conducted a pilot study using the system with groups of high school children in Japan and Australia and our results show the potential of the system to drive children’s interest in communicating, sharing, and discussing cultural themes with their remote peers through the social robot.

In this paper, we identify a set of socio-emotional cues and signals that are promoted by a tabletop social mediator robot in the context of a school setting. The robot adopts different roles to enhance such socio-emotional features, consequently aiding their identification by a structured annotation system. Various socio-emotional signals were observed for different robot roles, as well as different cues (gaze, speech). Future work will analyze cultural nuances as it expands the pilot to more schools worldwide.

With the increase in life expectancy and staff shortage, there is an urgency to understanding the needs of older adults and exploring emerging fields such as social robotics to tackle the challenges of ageing. The paper highlights the importance of providing cognitive support, physical support, reducing loneliness and increasing social engagement among older adults as well as reducing caregiver burden, and suggests that socially assistive robots (SAR) can assist older adults and their carers with such needs. However, the paper also points out that there are several challenges associated with designing and deploying SAR systems, and involving end-users in the design process is necessary to improve user acceptability and adoption. The paper describes the approaches used by PAL Robotics to facilitate real-world deployment of its ARI and TIAGo social robots, and provides examples of how these robots have been used to tackle different healthcare needs.

This paper introduces a Behaviour Tree based design of long-term social robot behaviour in the context of SHAPES project, using ROS-compatible libraries, specifically two types of behaviours: a robot idle behaviour where the human approaches and begins the interaction, and a second behaviour where the robot actively navigates and searchers for a specific user to deliver a reminder. The behaviours will be tested on-site as part of SHAPES pilots and adjusted based on feedback and needs and is focused on long-term robot acceptance.

With the world population ageing and with the number of healthcare users needing assistance and support increasing, healthcare is becoming more costly and, as such, the need to optimise and support independent living for older people is of paramount importance. This paper reviews the user requirements and challenges that are relevant to older adults who prefer to age healthily at home, and how socially assistive robots (SAR) can be used to help them. The main focus is placed on the social robotic application developed for the H2020 SHAPES project to promote Smart Living Environment for healthy ageing. The solution is based on the newest PAL Robotics’ robot ARI, a high-performance social robot and companion designed for a wide range of multi-modal expressive gestures, gaze and personalised behaviour, which is integrated via several Digital Solutions developed within the SHAPES project to improve human-robot interaction and user acceptability for independent living support tasks. The validation process will take place over the coming months at Clinica Humana (Mallorca, Spain), which is a private clinic that provides hospital care to retirement homes, communities and home-bound patients. A description of the scenario definition is presented in the paper, together with the validation plan that will be executed during the pilot assessments and the measures that will be taken to improve user engagement.

With the world population aging and the number of healthcare users with multiple chronic diseases increasing, healthcare is becoming more costly, and as such, the need to optimise both hospital and in-home care is of paramount importance. This paper reviews the challenges that the older people, people with mobility constraints, hospital patients and isolated healthcare users face, and how socially assistive robots can be used to help them. Related promising areas and limitations are highlighted. The main focus is placed on the newest PAL Robotics' robot: ARI, a high-performance social robot and companion designed for a wide range of multi-modal expressive gestures, gaze and personalised behaviour, with great potential to become part of the healthcare community by applying powerful AI algorithms. ARI can be used to help administer first-care attention, providing emotional support to people who live in isolation, including the elderly population or healthcare users who are confined because of infectious diseases such as Covid-19. The ARI robot technical features and potential applications are introduced in this paper.