Experiential artificial intelligence (AI) is an approach to the design, use, and evaluation of AI in cultural or other real-world settings that foregrounds human experience and context. It combines arts and engineering to support rich and intuitive modes of model interpretation and interaction, making AI tangible and explicit. The ambition is to enable significant cultural works and make AI systems more understandable to nonexperts, thereby strengthening the basis for responsible deployment. This paper discusses limitations and promising directions in explainable AI, contributions the arts offer to enhance and go beyond explainability, and methodology to support, deepen, and extend those contributions.

Repetitive, individual exercises can improve the functional ability of stroke survivors over the long term. With the aim of providing extra motivation to adhere to repetitive, individual rehabilitation, this paper presents a robotic coach for stroke rehabilitation. Our system uses the Pepper robot and performs one of twelve data-driven coaching policies. The policies were learned from human-human observations of professional stroke physiotherapists and provide high-level personalisation based on user information and training context. A within subjects evaluation of the system was conducted in-person involving short interactions with 3 stroke survivors. The system was able to engage the target end users and there were indications that decreased workload could be possible when using the system compared to exercising alone.

This full-day workshop addresses the problems of accessibility in HRI and the interplay of ethical considerations for disability-centered design and research, accessibility concerns for disabled researchers, and the design of assistive HRI technologies. We invite authors to submit extended abstracts (up to 2 pages, excluding references) and short papers (up to 4 pages, excluding references) on a range of topics relevant to ethics, accessibility, and assistive applications in HRI, including critical reflections on methodologies, design papers on human-centered or anti-ableist assistive technology, and papers from those outside the HRI community who may have insight to share on these concerns. The workshop will use a hybrid format to allow participants who due to disability, geographic, financial, or other constraints, are unable to travel, and will feature keynote speakers, panel discussions, and breakout sessions.

Assistive robots are expected to contribute to the solution of major societal problems in healthcare, such as the increasing number of elderly who need informal and professional care over a long period of time. Most of the research focuses on the development of humanlike robots to facilitate human-robot interaction and strengthen the social, cognitive and affective processes. However, there are some possible downsides of this type of "robot humanizing", like raising high expectations and causing incorrect mental models of the robots. Machine-like robots, on the other hand, may help to build more realistic mental models and expectations but might bring about less fluent interactions and less pronounced experiences (i.e., less to remember). To test if a human-like robot indeed brings about better interaction fluency and memory recall, we designed two types of robots for a joint human-robot music listening activity: A human-like and a machine-like robot (Pepper). Thirty students participated in the experiment managed by a Wizard-of-Oz set-up. As expected, the human-like robot proved to perform better in terms of fluency and memory recall. Currently, we are preparing a follow-up experiment, consisting of longer sessions with the elderly to see whether this effect persists for this age group and how far the human- or machine-likeness influences the elderly's understanding and expectations of the robot's capabilities.

In the original publication of this article, the affiliation information of two authors was inadvertently published incorrectly. Please find the correct affiliation information below: Antonio Fleres 1PhD School for Communication Studies, IULM University, via Carlo Bo 1, 20143 Milan, Italy Luisa Damiano 4Department of Communication, Arts and Media “Giampaolo Fabris”, IULM University, via Carlo Bo 1, 20143 Milan, Italy Springer wishes to apologize for the inconvenience caused.

This article proposes an integrative approach to robotics research, based on bringing interdisciplinarity into the lab. Such an approach will facilitate researchers across various fields in gaining a more nuanced understanding of technology, how it is developed, and its potential impacts. We describe how a philosopher spent time embedded in robotics labs in different European countries as part of an interdisciplinary team, gaining insights into their work and perspectives, including how robotics researchers view ethical issues related to robotics research. Focusing on issues raised by the EU Parliamentary Motion on Robotics, we developed a seminar and questionnaire that investigated questions of ethics, electronic personhood and the role of policy in research ethics. Our findings highlight that while robotics researchers care about the ethical implications of their work and support policy that addresses ethical concerns, they believe there to be significant misunderstandings in how policy makers view robotics and AI, as well as a lack of understanding of, and trust in, the role that experts outside of robotics can play in regulating robotics research effectively. We propose that an integrative approach can break down these misunderstandings by demystifying the way that knowledge is created across different fields.

We outline two points of criticism. Firstly, we argue that robots do constitute a separate category of beings in people's minds rather than being mere depictions of non-robotic characters. Secondly, we find that (semi-)automatic processes underpinning communicative interaction play a greater role in shaping robot-directed speech than Clark and Fischer's theory of social robots as depictions indicate.

There are numerous strategies for reducing the stress and anxiety associated with pain that children experience before and after surgery. There is a potential communication barrier between hospital staff and the child which may result in inadequate pain management. Social robots may reduce the gap between the support that personnel can provide and what the children's emotional needs are. This study qualitatively evaluates the interactions between children and their parents who interact with the social robot MiRo-E. In the overall interaction, the robot would act like a pet and show different behaviours based on the estimated pain level of the children. However, in the current study, only the quality of the robot interaction behaviours was tested with healthy children and no pain was measured. During this study, two usability tests were done. Each usability test evaluated a different robot interaction. In both tests, children and their parents evaluated the designed interactions. Results indicate that children initially have different responses to the robot. They can either be held back from immediately interacting or they are not afraid of the robot at all and start touching it and interacting immediately. Although the intended behaviours could be more elaborate and personalized, both children and their parents appeared to like the different emotions shown by the robot and how it responded to their touch. The parents also offered some ideas to enhance the interaction between a child and a robot in a medical context, such as by including more sounds, making some behaviours more distinct, and allowing kids to customize the robot's look.

There is a growing interest in implementing robotics applications for children in healthcare to provide companionship, comfort, education, and therapy. Parental expectations regarding robotics for young children play a critical role in influencing its development and acceptance. However, parental expectations are widely overlooked in HRI. Therefore, a better understanding of what parents of young children expect the robot to do in health-related interactions with robots is needed. To achieve this, we adopted the Technology-Specific Expectation Scale (TSES) [2] and added three more dimensions (i.e., assistive role, social-emotional, and playful distraction) to gauge usersf expectations of robots in healthcare, resulting in TSES-R. This paper reports the development and reliability analysis of TSES-R. Furthermore, this paper presents the preliminary results collected from using the TSES-R with a sample of 31 families, which showcases how these outcomes could be helpful for future related studies.

Discrimination and bias are pressing issues of many AI and robotics applications. These outcomes may derive from limited datasets that do not fully represent society as a whole or from the AI scientific community's western-male configuration bias. Although being a pressing issue, understanding how robotic systems can replicate and amplify inequalities and injustice among underrepresented communities is still in its infancy among social science and technical communities. This workshop contributes to filling this gap by exploring the research question: What do diversity and inclusion mean in the context of Human-Robot Interaction (HRI)? Here, attention is directed to three different levels of HRI: the technical, the community, and the target user level. Overall, this workshop will focus on the idea that AI systems can be created to be more attuned to inclusive societal needs, respect fundamental rights, and represent contemporary values in modern societies by integrating diversity and inclusion considerations.

There is growing interest in psychological interventions using socially assistive robots to mitigate distress and pain in the pediatric population. This work seeks to address the deficit in understanding of what features and functionality young children and their parents desire to help with pain management by using co-design, a common approach to exploring participants' imaginations and gathering design requirements. To close this gap, we carried out a co-design workshop involving seven families (with children aged between 4-6 and their parents) to understand their expectations and design preferences for a robot designed for pain management in children. Data were collected from surveys, video and audio recordings, interviews, and field notes. We present the robot prototypes constructed during the workshops and derive several preferences of the children (e.g, zoomorphic shape, distractors and emotional expressions as behaviors). Additionally, we report methodological insights regarding the involvement of young children and their parents in the co-design process. Based on the findings of this co-design study, we discuss personalization as a possible design concept for future child-robot interaction development.

Adherence to repetitive rehabilitation exercises is important in motor recovery after stroke. Similarly, repetitive solo practice exercises can improve the skill level of sports players. In both of these scenarios, regular human coaching has benefits, but in practice, the required training is often carried out alone, resulting in lowered adherence. This work presents a mixed methodology approach, novel in the context of designing for HRI, towards informing the design of a personalised robotic coach for stroke rehabilitation and squash. Using observations of human-human interactions, we first obtained action sequences of behaviours exhibited by coaches and physiotherapists. We then clustered these action sequences into behaviour graphs, with each graph representing a coaching policy usable for robotic control. Next we obtained coaches' and physiotherapists' reflections on the graphs' applicability to the real world. Finally, we provide an explanation of how the policies visualised in these graphs could be used for robotic control.