Conveying emotions via touch (i.e., affective touch) is a crucial part of social interaction and bonding. Socially assistive robots have widely used affective touch. However, drones being aerial robots, touch has only been explored as input for control, navigation, or command. Thus, this work investigates the potential of affective touch using a perched socially assistive drone. We address two critical topics: (1) using affective touch as a modality with drones and (2) implementing effective touch gestures for emotional communication with drones. In an user study (N = 30), we explored participants’ preferences for interaction modalities and mapped touch gestures to emotions. Results show that touch was the most preferred modality. Afterward, participants selected touch gestures to convey 16 emotions to a drone. We determined which gesture best fits each emotion. Our work highlights the relevance of affective haptics and sheds light on the nature of touch gestures’ emotional conveyance in Human-Drone Interaction.

The growing presence of drones in human spaces has sparked curiosity regarding their role as social creatures. One approach to conveying the social aspects of robotic devices is to incorporate emotions. However, the social effects of emotions depend on their perceived appropriateness by humans. In this work, we investigate the psychological effects of appropriate vs. inappropriate emotions displayed on a drone in a delivery scenario. Through an online study (N =97), we observe significant differences in how people ascribe social attributes to a drone, understand both its functional acceptance (e.g., ease of use, usefulness), social acceptance (e.g., drone as an interaction partner), and assess its social competencies and human-like attributes. Overall, for a given situation of interaction, the drone is perceived more (resp. less) positively when displaying appropriate (resp. inappropriate) emotions. We conclude with a discussion on the use of emotions in drones and their psychological effects on users. This work contributes to a deeper understanding of emotion appropriateness and social interactions in robotics.

Responsible AI design is increasingly seen as an imperative by both AI developers and AI compliance experts. One of the key tasks is envisioning AI technology uses and risks. Recent studies on the model and data cards reveal that AI practitioners struggle with this task due to its inherently challenging nature. Here, we demonstrate that leveraging a Large Language Model (LLM) can support AI practitioners in this task by enabling reflexivity, brainstorming, and deliberation, especially in the early design stages of the AI development process. We developed an LLM framework, ExploreGen, which generates realistic and varied uses of AI technology, including those overlooked by research, and classifies their risk level based on the EU AI Act regulation. We evaluated our framework using the case of Facial Recognition and Analysis technology in nine user studies with 25 AI practitioners. Our findings show that ExploreGen is helpful to both developers and compliance experts. They rated the uses as realistic and their risk classification as accurate (94.5%). Moreover, while unfamiliar with many of the uses, they rated them as having high adoption potential and transformational impact.

Research in affective robotics has been using emotions to improve human-robot interaction. One important aspect has been to design recognizable and believable emotions in robotics. Recent work argued that externally displayed emotions on robots may or may not be appropriate for a given situation. However, the selection of emotions as appropriate/inappropriate is not trivial. We here examine the practicality of an established model to craft for emotion appropriateness based on situations of interaction. To do so, we explored the use of the Ortony, Clore, and Collins (OCC) model, which provides a psychological framework of appraisal in which the characteristics of situations are defined and connected to emotions, to identify emotion categories and create contrasting perceptions of emotion appropriateness. We then mapped these categories to four recognizable emotions on aerial robots and designed two video clips (3min35s each) of respectively appropriate and inappropriate emotions. The clips were evaluated in an online study ( N =100) where significant differences were found in attitudes toward the robot's emotions. This paper contributes initial findings to designing for emotion appropriateness.

Assigning lifelike qualities to robotic agents (Anthropomorphism) is associated with complex affective interpretations of their behavior. These anthropomorphized perceptions are traditionally elicited through robots' designs. Yet, aerial robots (or drones) present a special case due to their – traditionally – non-anthropomorphic design, and prior research shows conflicting evidence on their perception as either person-like, animal-like, or machine-like. In this work, we explore how people perceive drones in a cross-dimensional space between these three dimensions by varying the affective state presented on the drone. To capture these perceptions, we developed a novel measurement instrumentAnZoMa. We describe the design, use, and deployment of the instrument in an online study (N=98). The study results suggest that different drone emotions triggered people to attribute various characteristics to the drone (e.g., interaction metaphors, traits, and features) and variations in acceptability of drone affective states. These results demonstrate the interdependencies between affective perceptions and anthropomorphism of drones. We conclude by discussing the necessity to integrate cross-dimensional perception of anthropomorphism in human-drone interaction and affective computing. This work contributes a novel tool to measure the dimensions and gravity of anthropomorphism and insights into interdependencies between different affective states displayed on drones and their anthropomorphized perception.

Interacting with flying objects has fueled people’s imagination throughout history. Over the past decade, the Human-Drone Interaction (HDI) community has been working towards making this dream a reality. Despite notable findings, we lack a high-level perspective on the current and future use cases for interacting with drones. We present a holistic view of domains and applications of use that are described, studied, and envisioned in the HDI body of work. To map the extent and nature of the prior research, we performed a scoping review (N=217). We identified 16 domains and over 100 applications where drones and people interact together. We then describe in depth the main domains and applications reported in the literature and further present under-explored use cases with great potential. We conclude with fundamental challenges and opportunities for future research in the field. This work contributes a systematic step towards increased replicability and generalizability of HDI research.

Drones are rapidly populating human spaces, yet little is known about how these flying robots are perceived and understood by humans. Recent works suggested that their acceptance is predicated upon their sociability. This paper explores the use of facial expressions to represent emotions on social drones. We leveraged design practices from ground robotics and created a set of rendered robotic faces that convey basic emotions. We evaluated individuals’ response to these emotional facial expressions on drones in two empirical studies (N = 98, N = 98). Our results demonstrate that individuals accurately recognize five drone emotional expressions, as well as make sense of intensities within emotion categories. We describe how participants were emotionally affected by the drone, showed empathy towards it, and created narratives to interpret its emotions. As a consequence, we formulate design recommendations for social drones and discuss methodological insights on the use of static versus dynamic stimuli in affective robotics studies.

Drone technologies represent a new category of mobile devices that are increasingly present in public spaces. They are becoming increasingly autonomous, featuring a wide range of capabilities from detecting objects to monitoring situations. Yet, little is known about the characteristics that influence their acceptability in public spaces. In this work, we investigate how people’s attitude towards drone capabilities is influenced by the context in which they are operating. We present three user studies: first, a participatory design study (N=5) in which we investigated people’s expectations towards drone capabilities and contexts of use; second, a pre-study (N=18) performed to select 6 contexts of use for public drones with three different severity levels; and third, a survey-based study (N=26) where we evaluated people’s attitude towards 10 drone capabilities in 6 contexts of varying severity levels. Our results demonstrate that people’s attitude towards drone capabilities is more positive for severe contexts. In addition, we found positive correlations for all capabilities between attitude and perceived severity of context. This work contributes to the design of context-sensitive human-drone interactions and to the future integration of public drones.