Objectives: Children with cancer often experience sleep problems, which are associated with many negative physical and psychological health outcomes, as well as with a lower quality of life. Therefore, interventions are strongly required to improve sleep in this population. We evaluated interactive education with respect to sleep hygiene with a social robot at a pediatric oncology outpatient clinic regarding the feasibility, experiences, and preliminary effectiveness. Methods: Researchers approached children (8 to 12 years old) who were receiving anticancer treatment and who were visiting the outpatient clinic with their parents during the two-week study period. The researchers completed observation forms regarding feasibility, and parents completed the Children’s Sleep Hygiene Scale before and two weeks after the educational regimen. The experiences of children and parents were evaluated in semi-structured interviews. We analyzed open answers by labeling each answer with a topic reflecting the content and collapsed these topics into categories. We used descriptive statistics to describe the feasibility and experiences, and a dependent-samples t-test to evaluate the preliminary effectiveness. Results: Twenty-eight families participated (58% response rate) and all interactions with the robot were completed. The children and parents reported that they learned something new (75% and 50%, respectively), that they wanted to learn from the robot more often (83% and 75%, respectively), and that they applied the sleeping tips from the robot afterwards at home (54%). Regarding the preliminary effectiveness, children showed a statistically significant improvement in their sleep hygiene (p = 0.047, d = 0.39). Conclusions: Providing an educational regimen on sleep hygiene in a novel, interactive way by using a social robot at the outpatient clinic seemed feasible, and the children and parents mostly exhibited positive reactions. We found preliminary evidence that the sleep hygiene of children with cancer improved.

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Background: Society is facing a global shortage of 17 million health care workers, along with increasing health care demands from a growing number of older adults. Social robots are being considered as solutions to part of this problem. Objective: Our objective is to evaluate the quality of care perceived by patients and caregivers for an integrated care pathway in an outpatient clinic using a social robot for patient-reported outcome measure (PROM) interviews versus the currently used professional interviews. Methods: A multicenter, two-parallel-group, nonblinded, randomized controlled trial was used to test for noninferiority of the quality of care delivered through robot-assisted care. The randomization was performed using a computer-generated table. The setting consisted of two outpatient clinics, and the study took place from July to December 2019. Of 419 patients who visited the participating outpatient clinics, 110 older patients met the criteria for recruitment. Inclusion criteria were the ability to speak and read Dutch and being assisted by a participating health care professional. Exclusion criteria were serious hearing or vision problems, serious cognitive problems, and paranoia or similar psychiatric problems. The intervention consisted of a social robot conducting a 36-item PROM. As the main outcome measure, the customized Consumer Quality Index (CQI) was used, as reported by patients and caregivers for the outpatient pathway of care. Results: In total, 75 intermediately frail older patients were included in the study, randomly assigned to the intervention and control groups, and processed: 36 female (48%) and 39 male (52%); mean age 77.4 years (SD 7.3), range 60-91 years. There was no significant difference in the total patient CQI scores between the patients included in the robot-assisted care pathway (mean 9.27, SD 0.65, n=37) and those in the control group (mean 9.00, SD 0.70, n=38): P=.08, 95% CI -0.04 to 0.58. There was no significant difference in the total CQI scores between caregivers in the intervention group (mean 9.21, SD 0.76, n=30) and those in the control group (mean 9.09, SD 0.60, n=35): P=.47, 95% CI -0.21 to 0.46. No harm or unintended effects occurred. Conclusions: Geriatric patients and their informal caregivers valued robot-assisted and nonrobot-assisted care pathways equally.

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With the advent of social robots which are designed to be 'social', human-like interactions have become a necessity. It is natural for us to use a plethora of emotions to convey additional information or to make an interaction more engaging. But emotion expression is not commonly associated with robots. Many humanoid robots cannot generate facial expressions to portray various emotions. Studies have shown that robots are multi-modal systems which can employ multiple channels to express an emotion. This thesis explores the non-verbal emotion expression techniques and their expressive capabilities. We found that some emotions are easier to express than others, and a single technique cannot express all the emotions. We chose a few emotions and systematically determined the best technique for each of them.

A young boy with type 1 Diabetes Mellitus is supported by a social robot on the road to self-management. The robot has knowledge on the goals that the boy needs to reach, as discussed with his health care professional. The robot also knows the boy’s activity options and preferences. It suggests activities based on this knowledge, but also encourages the boy to try new approaches. For parents, such a social robot means that they can be less teacher and more parent and for the health care professionals it means they can focus on the emotional aspects instead of the knowledge aspects during visits. Finally, the boy sees the robot as something that is fun and a peer in contrast to someone/something with a higher authority. The robot supports relatedness and a feeling of competence, the different activities provide a feeling of autonomy, and less budding of the parents reduces stress for the whole family. This all supports that the boy sees diabetesas his own responsibility and feels that he has enough competence and autonomy to take care of diabetes himself. In support of this vision we look in this thesis at the design and evaluation of a social robot.@en

Humanoid robots are becoming part of modern societies mainly involving the education and entertainment fields. Therefore, providing techniques that enable a humanoid to autonomously play instruments like the metallophone we enrich its usefulness.

In this work we present an approach for making humanoid robot NAO play the metallophone. Specifically, we provide an application which can be used by everyone owned a NAO robot. In our approach, no markers are necessary to be attached on the robot, making the procedure of playing music, seems natural. We propose an efficient real-time approach for recognizing and estimate the metallophone in 3D space using a single camera. Our vision approach performs well in camera displacements, illumination and aspect changes.

For accurate playing, the robot automatically validates the computed IK configurations based on a vision-based robot control approach and adapts its arm configurations if it is necessary. To achieve this, we rely on the estimated pose of the musical instrument and the detection of the beaters in 2D, based on natural features and then we apply inverse kinematics. Concerning kinematics, we use singularity-robust approach by pre-computing forward kinematics which are used to efficiently solve inverse kinematics (IK) problems.

In our approach we limited on moving only NAO's arms for playing the metallophone. Therefore, we developed a human-robot interaction phase in which the robot uses its perception about the position of the instrument in 3D space and it guides a human in order to place it in a reachable for the robot position. Moreover, in our system we provide a simple way to add songs, which NAO can immediately play.

The elicitation of user engagement is one of the current challenges of human-robot interaction, alongside with the identification of appropriate metrics to evaluate users' experience. Several studies focused on strategies aimed at maintaining engagement throughout an interaction, but limited research has been done on how to initiate it by drawing users' attention. The use of social cues in nonverbal human-human communication has been identified as a reliable source of information to determine if a person is engaged. These social cues can be used not only to understand more about human behavior, but also to design robot behaviors that can successfully draw the attention of humans. In this project we look to investigate what are effective techniques to draw attention and elicit initial engagement with a social robot at the entrance of a building. The robot proposed to display these behaviors is the humanoid robot Pepper, from SoftBank Robotics, as it has been specifically designed for human-robot interaction. Initially, the on-board functionalities of the robot are going to be tested. Secondarily, state-of-the-art techniques are going to extend those functionalities to improve Pepper's interactive skills. Eventually, robot behaviors are going to be designed and displayed to participants during an experiment. We aim at understanding the reactions of people to identify the most effective drawing attention behaviors and examine if the encounter with the robot is affected by a novelty effect. Different metrics are proposed to measure both these phenomena in our results. Our system is developed in Python with features extracted from Pepper's Naoqi framework.

The energy markets are emerging slowly towards a more decentralized form, e.g. technologies are allowing prosumers to trade energy from their solar house rooftops. But as these markets are governed by people and depend on the behavior of people directly, they are susceptible to challenges from the social, economic and cultural norms around the people. One of the example is the cultural discrimination in energy trading noted by some researchers and practitioners in rural India, where people do not prefer to trade energy and goods with different castes, especially lower castes. This creates an unfair market for the poor and lower caste producers, and thus this thesis aimed at modeling such markets through agent based modeling, and introduce different automation mechanisms, namely local mediation, and bid splitting to prevent the effects of discrimination in energy trading. Local mediation allows introduction of a mediator who prevents from direct trade between people and thus prevents any discrimination, bringing in some sort of anonymity. The other mechanism of bid split allows splitting the bids by producer and consumer into small chunks, such that 2 producers in market with 1 and 100 units of energy to trade would look like 101 producers in market trading 101 units of energy. Evaluating the results of these mechanisms on increase of social welfare, efficiency, market access and reduction of inequality has shown that the bid split helps in reducing the discrimination, and so does the introduction of mediation (where the mediator does not discriminate). The former has much more significant effect on the market access, and the validation of the model has helped derive some policy recommendations for implementer in the energy industry and governance field e.g. on the load management for the devices.

Today, off-the-shelf social robots are used increasingly in the HRI community to research social interactions with different target user groups across a range of domains (e.g. healthcare, education, retail and other public spaces). We invite everyone doing HRI studies with end users, in the lab or in the wild, to collect past experiences of methods and practices that had issues or did not turn out as expected. This could include but is not limited to experimental setup, unplanned interactions, or simply the difficulty in transferring theory to the real world. In order to be able to generalize and compare differences across multiple HRI domains and create common solutions, we are focusing in this workshop on experiences with often used off-the-shelf social robots. We are interested in identifying the underlying causes of the unexpected HRI results, e.g. the contextual, task, and user related factors that influence interaction with a robot platform. We will furthermore discuss and document (ad hoc) solutions and/or lessons learned such that they can be shared with the HRI community. As well as sharing specific case studies documenting real world HRI experiences, we further hope to inspire the continued sharing of open and insightful reflections within the HRI community.@en

Socially-aware robot navigation strives to find efficient methods to autonomously navigate known environments while incorporating social metrics derived from human behavior. Several methods built for navigation on dynamic and uncertain environments have been adapted to resemble human navigation, but fail to appropriately reflect the social characteristics of human decision making.

A reliable solution for this problem is found in model-based methods working as local or global planners. Model-based research on human aware navigation focuses on two specific alternatives. The first option corresponds to models that apply social-psychology and cognitive sciences to create human-like behavior, where the Social Force Model is the predominant approach. The second alternative is related to models that use machine learning to copy human-like characteristics into mathematical models.

The former approach has proven to be efficient in human-aware navigation, but further studies are required to analyze the expansion of this method to more complex human-interactive navigation. In this thesis project, we look to test the Modified Extended Social Force Model (MESFM) to implement a guiding behavior on a humanoid robot. The MESFM incorporates a new force linked to the guided person to maintain a natural distance between both subjects, while generating smooth navigational maneuvers.

In addition, whether caused by sensor failure or occlusion incidents, the event of losing track of the guided person has a good probability of occurring. This scenario is studied by extending the high level control of the robotic system with a searching mode.

The architecture proposed to control the guiding behavior and the searching mode exploits the design of the humanoid robot Pepper, from SoftBank Robotics, to incorporate human-like gestures and increase the interaction between robot and human.

We aim to apply this behavior as an Office-Guide Robot and test this solution to understand the reaction of people involved in the guiding task though subjective and objective metrics. Our system is developed on top of the open-software Robot Operative System (ROS) with features extracted from Pepper's Naoqi framework.

A good explanation takes the user who is receiving the explanation into account. We aim to get a better understanding of user preferences and the differences between children and adults who receive explanations from a robot. We implemented a Nao-robot as a belief-desire-intention (BDI)-based agent and explained its actions using two different explanation styles. Both are based on how humans explain and justify their actions to each other. One explanation style communicates the beliefs that give context information on why the agent performed the action. The other explanation style communicates the goals that inform the user of the agent's desired state when performing the action. We conducted a user study (19 children, 19 adults) in which a Nao-robot performed actions to support type 1 diabetes mellitus management. We investigated the preference of children and adults for goalversus belief-based action explanations. From this, we learned that adults have a significantly higher tendency to prefer goal-based action explanations. This work is a necessary step in addressing the challenge of providing personalised explanations in human-robot and human-agent interaction.@en

We address the question of how an agent can adapt its behavior to comply with newly adopted norms. This is particularly relevant in the case of open systems where agents may enter and leave norm-governed social contexts not known at design time. This requires norms to be explicitly and separately stated and presented to an agent as rules to which it then can try to adapt its behavior. We propose a formal semantic framework that specifies an execution mechanism for such socially adaptive agents. This framework is based on expressing norms using Linear Temporal Logic. The formality of the framework allows us to rigorously study its norm compliance properties. A weak form of norm compliance allows agents to abort execution in order to prevent norm violation. In this paper we investigate a stronger notion of norm compliance that is evaluated over infinite traces. We show that it is not possible for all agents to be strongly compliant with any arbitrary set of norms. We then investigate situations when strong norm compliance can be guaranteed.@en