The objective of this study was to identify technological solutions that could assist healthcare professionals in promoting physical activity and exercise among geriatric patients and to reinforce the behavioural changes initiated by healthcare professionals, with the aim of enhancing the overall therapeutic outcomes. A design thinking strategy was employed to guide the investigation. Initially, a field test was conducted to assess the necessity and effectiveness of the physical presence of robots in therapeutic interactions. Three technological solutions were subjected to testing: a Pepper robot, a mobile avatar, and a stationary avatar. The findings indicated that while Pepper was the most favoured by patients, its physical presence did not offer significant benefits over the avatars, given the higher costs and the challenges posed by the hospital environment. The findings of the field test informed the two subsequent design thinking workshops. The workshops were conducted by multidisciplinary teams comprising healthcare professionals with expertise in geriatric care, physiotherapy students, robotics engineers, and human factors engineers. The design thinking workshops yielded a variety of innovative solutions, which emphasised the importance of continuous patient monitoring and the ability to position the solution within the patient’s field of view. Subsequently, the solutions generated during the workshops were subjected to evaluation by the same multidisciplinary team. The evaluation process indicated that the optimal and most feasible solution was an avatar displayed on a screen, mounted on an actively movable robot arm attached to the wall. The findings of this study offer valuable insights into the potential use of technological solutions in promoting physical activity and exercise among geriatric patients in acute care hospitals. Future research should concentrate on validating these findings through larger-scale studies. This should include an examination of the long-term impacts of avatar-based interactions on patient health and recovery, as well as an investigation of the ethical implications and privacy concerns associated with continuous patient monitoring.

In human–robot interaction studies, trust is often defined as a process whereby a trustor makes themselves vulnerable to a trustee. The role of vulnerability however is often overlooked in this process but could play an important role in the gaining and maintenance of trust between users and robots. To better understand how vulnerability affects human–robot trust, we first reviewed the literature to create a conceptual model of vulnerability with four vulnerability categories. We then performed a meta-analysis, first to check the overall contribution of the variables included on trust. The results showed that overall, the variables investigated in our sample of studies have a positive impact on trust. We then conducted two multilevel moderator analysis to assess the effect of vulnerability on trust, including: (1) an intercept model that considers the relationship between our vulnerability categories and (2) a non-intercept model that treats each vulnerability category as an independent predictor. Only model 2 was significant, suggesting that to build trust effectively, research should focus on improving robot performance in situations where the users are unsure how reliable the robot will be. As our vulnerability variable is derived from studies of human–robot interaction and researcher reflections about the different risks involved, we relate our findings to these domains and make suggestions for future research avenues.

Abstract Cognitive load has been widely studied to help understand human performance. It is desirable to monitor user cognitive load in applications such as automation, robotics, and aerospace to achieve operational safety and to improve user experience. This can allow efficient workload management and can help to avoid or to reduce human error. However, tracking cognitive load in real time with high accuracy remains a challenge. Hence, we propose a framework to detect cognitive load by non-intrusively measuring physiological data from the eyes and heart. We exemplify and evaluate the framework where participants engage in a task that induces different levels of cognitive load. The framework uses a set of classifiers to accurately predict low, medium and high levels of cognitive load. The classifiers achieve high predictive accuracy. In particular, Random Forest and Naive Bayes performed best with accuracies of 91.66% and 85.83% respectively. Furthermore, we found that, while mean pupil diameter change for both right and left eye were the most prominent features, blinking rate also made a moderately important contribution to this highly accurate prediction of low, medium and high cognitive load. The existing results on accuracy considerably outperform prior approaches and demonstrate the applicability of our framework to detect cognitive load.

Games have the potential to be used to enable the general public to understand the application and implication of many forms of work. We demonstrate an adaptive game that simulates the activities happening in extreme environments where robots carry out tasks and human operators control robots. Under the emergency, operators can experience high cognitive load (CL), so the game dynamically adjusts its support strategy to the user’s CL. We evaluated the adaptive game to find its effectiveness towards reducing user’s CL in real-time by comparing it with the non-adaptive and randomly adaptive versions of the game. The results showed that the method developed to dynamically adjusts the game-support strategy to the user’s CL was promising. The method reduced the CL significantly when compared to the randomly adaptive version, where the adaptations were randomly enabled or disabled on the game. The findings confirm the applicability of the method used to adapt the game and demonstrate the potential to be used to disseminate the work to the general public.

Affordances describe the possibilities for an agent to perform actions with an object. While the significance of the affordance concept has been previously studied from varied perspectives, such as psychology and cognitive science, these approaches are not always sufficient to enable direct transfer, in the sense of implementations, to artificial intelligence (AI)-based systems and robotics. However, many efforts have been made to pragmatically employ the concept of affordances, as it represents great potential for AI agents to effectively bridge perception to action. In this survey, we review and find common ground amongst different strategies that use the concept of affordances within robotic tasks, and build on these methods to provide guidance for including affordances as a mechanism to improve autonomy. To this end, we outline common design choices for building representations of affordance relations, and their implications on the generalisation capabilities of an agent when facing previously unseen scenarios. Finally, we identify and discuss a range of interesting research directions involving affordances that have the potential to improve the capabilities of an AI agent.

Public perceptions of Robotics and Artificial Intelligence (RAI) are important in the acceptance, uptake, government regulation and research funding of this technology. Recent research has shown that the public's understanding of RAI can be negative or inaccurate. We believe effective public engagement can help ensure that public opinion is better informed. In this paper, we describe our first iteration of a high throughput in-person public engagement activity. We describe the use of a light touch quiz-format survey instrument to integrate in-the-wild research participation into the engagement, allowing us to probe both the effectiveness of our engagement strategy, and public perceptions of the future roles of robots and humans working in dangerous settings, such as in the off-shore energy sector. We critique our methods and share interesting results into generational differences within the public's view of the future of Robotics and AI in hazardous environments. These findings include that older peoples' views about the future of robots in hazardous environments were not swayed by exposure to our exhibit, while the views of younger people were affected by our exhibit, leading us to consider carefully in future how to more effectively engage with and inform older people.

Reasoning about object grasp affordances allows an autonomous agent to estimate the most suitable grasp to execute a task. While current approaches for estimating grasp affordances are effective, their prediction is driven by hypotheses on visual features rather than an indicator of a proposal's suitability for an affordance task. Consequently, these works cannot guarantee any level of performance when executing a task and, in fact, not even ensure successful task completion. In this work, we present a pipeline for self-assessment of grasp affordance transfer (SAGAT) based on prior experiences. We visually detect a grasp affordance region to extract multiple grasp affordance configuration candidates. Using these candidates, we forward simulate the outcome of executing the affordance task to analyse the relation between task outcome and grasp candidates. The relations are ranked by performance success with a heuristic confidence function and used to build a library of affordance task experiences. The library is later queried to perform one-shot transfer estimation of the best grasp configuration on new objects. Experimental evaluation shows that our method exhibits a significant performance improvement up to 11.7% against current state-of-the-art methods on grasp affordance detection. Experiments on a PR2 robotic platform demonstrate our method's highly reliable deployability to deal with real-world task affordance problems.

Autonomous vehicles and robots are increasingly being deployed to remote, dangerous environments in the energy sector, search and rescue and the military. As a result, there is a need for humans to interact with these robots to monitor their tasks, such as inspecting and repairing offshore wind-turbines. Conversational Agents can improve situation awareness and transparency, while being a hands-free medium to communicate key information quickly and succinctly. As part of our user-centered design of such systems, we conducted an in-depth immersive qualitative study of twelve marine research scientists and engineers, interacting with a prototype Conversational Agent. Our results expose insights into the appropriate content and style for the natural language interaction and, from this study, we derive nine design recommendations to inform future Conversational Agent design for remote autonomous systems.

There are many challenges when it comes to deploying robots remotely including lack of operator situation awareness and decreased trust. Here, we present a conversational agent embodied in a Furhat robot that can help with the deployment of such remote robots by facilitating teaming with varying levels of operator control.

To avoid putting humans at risk, there is an imminent need to pursue autonomous robotized facilities with maintenance capabilities in the energy industry. This paper presents a video of the ORCA Hub simulator, a framework that unifies three types of autonomous systems (Husky, ANYmal and UAVs) on an offshore platform digital twin for training and testing human-robot collaboration scenarios, such as inspection and emergency response.

Robotics for hazardous environments is currently an important area of research, with the ambition of reducing human risk in potentially devastating situations. Here, we are presenting a Modular Control Framework (MCF) for a low-cost robot with limited sensory resources to address this issue. As a proof of concept, we emulate 3 scenarios - (1) adaptive planning for obstruction avoidance (road block), (2) object identification and support-case-based behaviour adjustment (search and rescue) and (3) autonomous navigation through the environment with reporting of structural status (patrol and monitoring). These were implemented and validated using a Cozmo robot in a small-scale Lego environment. We found that our system can reroute in 90%, can help an injured person 80% and report about failing equipment in 80% of all tested cases, where most of the fails were caused by the object detection used. Our MCF is implemented using ROS, making it easy to use and adjust for other robotic platforms.

In this article we evaluate the incremental object learning approach of the iCub humanoid robot which is directed towards long-term engagement. Affordable robot companion systems are currently entering the consumer market which highlights the importance in understanding environmental influences on robotic systems under real world conditions. If a robot is to be sent into the real world or different robots/sensors are to be used, we need our algorithms to be independent from both illumination and sensor influenced changes. In our work, we investigate the robustness of the interactive object learning to linear and non-linear lighting changes which can occur due to illumination changes throughout the day or the sensors used. Our results with the models we use suggest that the current method is susceptible to these changes. Therefore, we provide an adjustment to the current method to be able to cope with this problem.

In this paper, we will explore human movement using a tutoring spotter system which controls an iCub robot. We will present an evaluation based on the captured human movement from an experimental study, where our participants demonstrated a salt-shaker and a cup-stacking task to the iCub robot. We will use a method of action recognition, which will help the robot to differentiate between these actions, as it will focus the robot's attention on the vital information presented by the human. Our findings imply that the behaviour of the robot affects our participants and it influences both, presentation time as well as the ratio between action and sub-action during the task presentation. Furthermore, the stability of the action recognition system is influenced by this modification of the human presentation.

The aim of this paper is to give a short overview on how facial expressions enhance interaction with elderly people. Two studies were done. The first one was an online questionnaire that gave information on what type of expressions were more appropriate and the second one was an experiment in which participants met the robot. This study's results suggested a correlation between facial expressions and interaction enhancement. Participants preferred a certain motor speed according to the facial expression presented which shows that speed has an influence on understanding expressions and beyond this, emotions. In the end, this paper presents some ways of enhancing interaction with the robot.

A fundamental challenge for robotics is to transfer the human natural social skills to the interaction with a robot. At the same time, neuroscience and psychology are still investigating the mechanisms behind the development of human-human interaction. HRI becomes therefore an ideal contact point for these different disciplines, as the robot can join these two research streams by serving different roles. From a robotics perspective, the study of interaction is used to implement cognitive architectures and develop cognitive models, which can then be tested in real world environments. From a neuroscientific perspective, robots could represent an ideal stimulus to establish an interaction with human partners in a controlled manner and make it possible studying quantitatively the behavioral and neural underpinnings of both cognitive and physical interaction. Ideally, the integration of these two approaches could lead to a positive loop: the implementation of new cognitive architectures may raise new interesting questions for neuroscientists, and the behavioral and neuroscientific results of the human-robot interaction studies could validate or give new inputs for robotics engineers. However, the integration of two different disciplines is always difficult, as often even similar goals are masked by difference in language or methodologies across fields. The aim of this workshop will be to provide a venue for researchers of different disciplines to discuss and present the possible point of contacts, to address the issues and highlight the advantages of bridging the two disciplines in the context of the study of interaction.

In this paper, a robot that is using politeness to overcome its incapability to serve is presented. The mobile robot “Alex” is interacting with human office colleagues in their environment and delivers messages, phone calls, and companionship. The robot's battery capacity is not sufficient to survive a full working day. Thus, the robot needs to recharge during the day. By doing so it is unavailable for tasks that involve movement. The study presented in this paper supports the idea that an incapability of fullfiling an appointed task can be overcome by politeness and showing appropriate behaviour. The results, reveal that, even the simple adjustment of spoken utterances towards a more polite phrasing can change the human's perception of the robot companion. This change in the perception can be made visible by analysing the human's behaviour towards the robot.

In this paper, we are presenting a human robot interaction study, which is targeting the question, how the attention system of the iCub robot is performing in comparison with infants. To answer this, we have studied a task presentation, towards infants, the iCub simulator and the iCub robot. We compared the gazing behavior of the recipients. In developmental robotics, as well as in tutoring situations, the gazing behavior of the recipient plays an important role for the following interaction.

In this paper, we investigate the role of physical embodiment of a robot and its degrees of freedom in HRI. Both factors have been suggested to be relevant in definitions of embodiment, and so far we do not understand their effects on the way people interact with robots very well. Linguistic analyses of verbal interactions with robots differing with respect to physical embodiment and degrees of freedom provide a useful methodology to investigate factors conditioning human-robot interaction. Results show that both physical embodiment and degrees of freedom influence interaction, and that the effect of physical embodiment is located in the interpersonal domain, concerning in how far the robot is perceived as an interaction partner, whereas degrees of freedom influence the way users project the suitability of the robot for the current task.

The paper investigates the effects of a robot's “on-line” feedback during a tutoring situation with a human tutor. Analysis is based on a study conducted with an iCub robot that autonomously generates its feedback (gaze, pointing gesture) based on the system's perception of the tutor's actions using the idea of reciprocity of actions. Sequential micro-analysis of two opposite cases reveals how the robot's behavior (responsive vs. non-responsive) pro-actively shapes the tutor's conduct and thus co-produces the way in which it is being tutored. A dialogic and a monologic tutoring style are distinguished. The first 20 seconds of an encounter are found to shape the user's perception and expectations of the system's competences and lead to a relatively stable tutoring style even if the robot's reactivity and appropriateness of feedback changes.