Séverin Lemaignan

Séverin Lemaignan, Charlotte Edmunds, Emmanuel Senft et al.

Evaluating human-robot social interactions in a rigorous manner is notoriously difficult: studies are either conducted in labs with constrained protocols to allow for robust measurements and a degree of replicability, but at the cost of ecological validity; or in the wild, which leads to superior experimental realism, but often with limited replicability and at the expense of rigorous interaction metrics. We introduce a novel interaction paradigm, designed to elicit rich and varied social interactions while having desirable scientific properties (replicability, clear metrics, possibility of either autonomous or Wizard-of-Oz robot behaviours). This paradigm focuses on child-robot interactions, and builds on a sandboxed free-play environment. We present the rationale and design of the interaction paradigm, its methodological and technical aspects (including the open-source implementation of the software platform), as well as two large open datasets acquired with this paradigm, and meant to act as experimental baselines for future research.

Xavier Alameda-Pineda, Angus Addlesee, Daniel Hernández García et al.

Despite the many recent achievements in developing and deploying social robotics, there are still many underexplored environments and applications for which systematic evaluation of such systems by end-users is necessary. While several robotic platforms have been used in gerontological healthcare, the question of whether or not a social interactive robot with multi-modal conversational capabilities will be useful and accepted in real-life facilities is yet to be answered. This paper is an attempt to partially answer this question, via two waves of experiments with patients and companions in a day-care gerontological facility in Paris with a full-sized humanoid robot endowed with social and conversational interaction capabilities. The software architecture, developed during the H2020 SPRING project, together with the experimental protocol, allowed us to evaluate the acceptability (AES) and usability (SUS) with more than 60 end-users. Overall, the users are receptive to this technology, especially when the robot perception and action skills are robust to environmental clutter and flexible to handle a plethora of different interactions.

Ferran Gebellí, Anaís Garrell, Jan-Gerrit Habekost et al.

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.

Séverin Lemaignan, Nigel Newbutt, Louis Rice et al.

For a period of three weeks in June 2021, we embedded a social robot (Softbank Pepper) in a Special Educational Needs (SEN) school, with a focus on supporting the well-being of autistic children. Our methodology to design and embed the robot among this vulnerable population follows a comprehensive participatory approach. We used the research project as a test-bed to demonstrate in a complex real-world environment the importance and suitability of the nine UNICEF guidelines on AI for Children. The UNICEF guidelines on AI for Children closely align with several of the UN goals for sustainable development, and, as such, we report here our contribution to these goals.

Katie Winkle, Emmanuel Senft, Séverin Lemaignan

Participatory Design (PD) in Human-Robot Interaction (HRI) typically remains limited to the early phases of development, with subsequent robot behaviours then being hardcoded by engineers or utilised in Wizard-of-Oz (WoZ) systems that rarely achieve autonomy. We present LEADOR (Led-by-Experts Automation and Design Of Robots) an end-to-end PD methodology for domain expert co-design, automation and evaluation of social robots. LEADOR starts with typical PD to co-design the interaction specifications and state and action space of the robot. It then replaces traditional offline programming or WoZ by an in-situ, online teaching phase where the domain expert can live-program or teach the robot how to behave while being embedded in the interaction context. We believe that this live teaching can be best achieved by adding a learning component to a WoZ setup, to capture experts' implicit knowledge, as they intuitively respond to the dynamics of the situation. The robot progressively learns an appropriate, expert-approved policy, ultimately leading to full autonomy, even in sensitive and/or ill-defined environments. However, LEADOR is agnostic to the exact technical approach used to facilitate this learning process. The extensive inclusion of the domain expert(s) in robot design represents established responsible innovation practice, lending credibility to the system both during the teaching phase and when operating autonomously. The combination of this expert inclusion with the focus on in-situ development also means LEADOR supports a mutual shaping approach to social robotics. We draw on two previously published, foundational works from which this (generalisable) methodology has been derived in order to demonstrate the feasibility and worth of this approach, provide concrete examples in its application and identify limitations and opportunities when applying this framework in new environments.

Youssef Mohamed, Séverin Lemaignan

Integrating real-time, complex social signal processing into robotic systems -- especially in real-world, multi-party interaction situations -- is a challenge faced by many in the Human-Robot Interaction (HRI) community. The difficulty is compounded by the lack of any standard model for human representation that would facilitate the development and interoperability of social perception components and pipelines. We introduce in this paper a set of conventions and standard interfaces for HRI scenarios, designed to be used with the Robot Operating System (ROS). It directly aims at promoting interoperability and re-usability of core functionality between the many HRI-related software tools, from skeleton tracking, to face recognition, to natural language processing. Importantly, these interfaces are designed to be relevant to a broad range of HRI applications, from high-level crowd simulation, to group-level social interaction modelling, to detailed modelling of human kinematics. We demonstrate these interface by providing a reference pipeline implementation, packaged to be easily downloaded and evaluated by the community.