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.@en

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.@en

Cognitive agent programming frameworks facilitate the development of intelligent virtual agents. By adding a computational model of emotion to such a framework, one can program agents capable of using and reasoning over emotions. Computational models of emotion are generally based on cognitive appraisal theory; however, these theories introduce a large set of appraisal processes, which are not specified in enough detail for unambiguous implementation in cognitive agent programming frameworks. We present CAAF (Cognitive Affective Agent programming Framework), a framework based on the belief-desire theory of emotions (BDTE), that enables the computation of emotions for cognitive agents (i.e., making them cognitive affective agents). In this paper we bridge the remaining gap between BDTE and cognitive agent programming frameworks. We conclude that CAAF models consistent, domain independent emotions for cognitive agent programming.@en

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

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

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

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

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

Nowadays, off-the-shelf social robots are used more frequently by the HRI community to research social interactions with different types of users across a range of domains such as education, retail, health care, public places and other domains. Everyone doing HRI research with end-users is invited to submit a case study to our workshop. We are particularly interested in case studies where things did not go as planned. Case studies describing research in the lab or in the wild are both welcome. Examples of unplanned experiences could include, but are not limited to, unexpected responses from the user, issues with the experimental setup or simply having challenges with transferring theory to the real world. In this workshop, we focus on off-the-shelf robots. In order to generalize and compare differences across multiple HRI domains and create common solutions, we will provide a template for your case study. We are interested in learning how such unexpected HRI results can be reported. In the workshop, we will discuss and study how failures are reported and be inspired to create a list of good ways to report failures, which can hopefully be inspiring for the HRI community.@en

Nowadays, off-the-shelf social robots are used more frequently by the HRI community to research social interactions with different types of users across a range of domains such as education, retail, health care, public places and other domains. Everyone doing HRI research with end-users is invited to submit a case study to our workshop. We are particularly interested in case studies where things did not go as planned. Case studies describing research in the lab or in the wild are both welcome. Examples of unplanned experiences could include, but are not limited to, unexpected responses from the user, issues with the experimental setup or simply having challenges with transferring theory to the real world. In this workshop, we focus on off-the-shelf robots. In order to generalize and compare differences across multiple HRI domains and create common solutions, we will provide a template for your case study. We are interested in learning how such unexpected HRI results can be reported. In the workshop, we will discuss and study how failures are reported and be inspired to create a list of good ways to report failures, which can hopefully be inspiring for the HRI community.@en

Patient Reported Outcome Measures (PROMs) are a means of collecting information on the effectiveness of care delivered to patients as perceived by the patients themselves. A patient's pain level is a typical parameter only a patient him/herself can describe. It is an important measure for a person?s quality of life. When a patient stays in a Dutch hospital, nursing staff needs to ask a patient for its pain level at least three times a day. Due to their work pressure, this requirement is regularly not met. A social robot available as a bed side companion for a patient during his hospital stay, might be able to ask the patient's pain level regularly. The video shows that this innovation in PROM data acquisition is feasible in older persons.@en

Patient Reported Outcome Measures (PROMs) are a means of collecting information on the effectiveness of care delivered to patients as perceived by the patients themselves. A patient's pain level is a typical parameter only a patient him/herself can describe. It is an important measure for a person?s quality of life. When a patient stays in a Dutch hospital, nursing staff needs to ask a patient for its pain level at least three times a day. Due to their work pressure, this requirement is regularly not met. A social robot available as a bed side companion for a patient during his hospital stay, might be able to ask the patient's pain level regularly. The video shows that this innovation in PROM data acquisition is feasible in older persons.@en

Pediatric oncology patients could benefit from bonding with a social robot and talking about their day in the hospital. With our research we aim to contribute to the development of a robot that is able to facilitate a child-robot bond autonomously and long-term. We propose to use robot-disclosure and a shared interaction history to create a child-robot bond where the child feels comfortable and familiar enough to talk about their day with the robot.@en