Dynamic Neural Fields (DNFs) offer a biologically grounded framework for modelling continuous-time neural dynamics underlying a wide range of cognitive and sensorimotor functions. Despite their expressive power, DNF architectures are typically designed and tuned manually, as their nonlinear dynamics and kernel-based interactions make systematic design and parameterisation difficult. As a result, constructing functional DNF models requires expert knowledge and iterative trial-and-error. We introduce NEAT-DNFs, a neuroevolutionary framework that extends NeuroEvolution of Augmenting Topologies (NEAT) to the automated synthesis of DNF architectures. In NEAT-DNFs, neural fields and inter-field interactions are encoded as evolvable genes, enabling the joint evolution of intrinsic parameters and architectural topology. This allows continuous-time neural dynamics to be discovered autonomously, without manual tuning. We evaluate NEAT-DNFs on a hierarchy of benchmark tasks of increasing complexity. Initial tasks target core Dynamic Field Theory (DFT) mechanisms, including detection, working memory, and selection. We then consider more demanding problems that require the emergence of intermediate neural fields, thereby testing the framework's capacity for structural innovation. Across all tasks, NEAT-DNFs reliably evolves compact and stable architectures, increasing structural complexity only when necessary. This work bridges neuroevolution and DFT, establishing a foundation for the automated design of biologically inspired neural field models.

Social robots are increasingly applied as health behavior change interventions, yet actionable knowledge to guide their design and evaluation remains limited. This systematic review synthesizes (1) the behavior change strategies used in existing HRI studies employing social robots to promote health behavior change, and (2) the evaluation methods applied to assess behavior change outcomes. Relevant literature was identified through systematic database searches and hand searches. Analysis of 39 studies revealed four overarching categories of behavior change strategies: coaching strategies, counseling strategies, social influence strategies, and persuasion-enhancing strategies. These strategies highlight the unique affordances of social robots as behavior change interventions and offer valuable design heuristics. The review also identified key characteristics of current evaluation practices, including study designs, settings, durations, and outcome measures, on the basis of which we propose several directions for future HRI research.

This study investigates how interaction scenarios of human caregiving for robots affect humans’ perceived bond with robots. In a between-subjects lab experiment (n = 88), participants played a game with a social robot during which they provided either 1) emotional care (comforting the robot); 2) instrumental care (helping with battery charging); or 3) no care for the robot. Results indicated that caregiving did not significantly affect human-robot bonding according to explicit relationship measures including closeness, social attraction, or desire for future interaction. However, caregiving mattered when bonding was measured implicitly. Those in the emotional caregiving scenario were more hesitant to replace the robot and invested more effort in a voluntary task requested by the robot than those who provided no care. These findings provide empirical evidence that emotional caregiving interactions can effectively foster initial human-robot bonding, highlighting a promising design scenario for human-robot interaction.

To design social robots to effectively promote health behavior change, it is essential to understand how people respond to various health communication strategies employed by these robots. This study examines the effectiveness of two types of social control strategies from a social robot-relationship-focused strategies (emphasizing relational consequences) and target-focused strategies (emphasizing health consequences)-in encouraging people to reduce sedentary behavior. A two-session lab experiment was conducted (n = 135), where participants first played a game with a robot, followed by the robot persuading them to stand up and move using one of the strategies. Half of the participants joined a second session to have a repeated interaction with the robot. Results showed that relationship-focused strategies motivated participants to stay active longer. Repeated sessions did not strengthen participants' relationship with the robot, but those who felt more attached to the robot responded more actively to the target-focused strategies. These findings offer valuable insights for designing persuasive strategies for social robots in health communication contexts.

As populations are ageing, innovative solutions are needed to support older adults in living independently. Socially Assistive Robots (SARs) hold significant potential, but their adoption in real-world healthcare settings remains limited, partially due to low user acceptance and diverse end-user needs. Therefore, personalization might be an interesting approach to address these user needs and increase user acceptance. This perspective paper presents a targeted synthesized analysis of findings from four prior studies conducted by the authors, to explore the role of personalized communication in SARs and its impact on user experience. The analysis reveals that both explicit and implicit personalization strategies enhance user engagement, acceptance, and communication effectiveness in SARs. It was found that explicit personalization primarily involves adjustments such as customizing speech characteristics, while implicit personalization focuses more on tailoring message content (e.g., based on individual interests of older adults). By linking empirical findings to existing literature, this paper provides a novel perspective on personalization approaches in SAR design, emphasizing the importance of both individual and group-level explicit and/or implicit adaptations to improve the user experience of SARs for older adults.

Health-related social control refers to intentional attempts to influence people's health behaviors, often seen in personal relationships. Social robots hold promise in influencing people's health by exerting health-related social control, but it is unclear which social control strategies used by robots are appropriate and potentially effective. This study investigates the effects of positive versus negative, and relationship-oriented versus target-oriented social control strategies from a social robot on people's psychological reactions. In an online video prototype study, participants viewed scenarios of a social robot attempting to change their sedentary behaviors by using different strategies. We found that positive (versus negative) strategies elicited stronger positive affect, enjoyment, and perceived social appropriateness, reduced perceived threats to freedom, and strengthened behavioral intention. Meanwhile, the relationship-oriented (versus target-oriented) strategies elevated people's negative affect, reduced enjoyment and perceived appropriateness, elevated perceived threats to freedom, and weakened behavioral intentions. Given these findings, we give recommendations for designing health influence strategies in social robots.

In virtual reality (VR) studies, where object distance plays a role of an independent variable, unknown egocentric distance perception values can affect the interpretation of the collected data. It is known that the perceived egocentric distance in VR is often underestimated, which may affect other judgments that implicitly depend on it. In order to prepare later experiments on the effect of distance on audiovisual (a)synchrony perception, this study quantifies the egocentric distance perception in a virtual indoor environment using two methods: verbal judgment (VJ) and position adjustment (PA). For the VJ method, participants verbally estimated the distance between their own position and a cardboard box position at a distance between a nominal 5 m and 30 m, with increments of 5 m. For the PA method, participants were asked to position a cardboard box to an instructed distance of a nominal 5 m to 13 m, with increments of 1 m. Both methods (VJ and PA) showed significant and substantial levels of underestimation, where simulated distance was underestimated on average by 38.5%. Our study suggests taking these findings into account when treating distance as an independent parameter in experiments conducted in purely simulated virtual spaces which do not exist physically in the real world.

Current research on external Human-Machine Interfaces (eHMIs) in facilitating interactions between automated vehicles (AVs) and pedestrians have largely focused on one-to-one encounters. In order for eHMIs to be viable in reality, they need to be scalable, i.e., facilitate interaction with more than one pedestrian with clarity and unambiguity. We conducted a virtual-reality-based empirical study to evaluate four eHMI designs with two pedestrians. Results show that even in this minimum criteria of scalability, traditional eHMI designs struggle to communicate effectively whom the AV intends to yield to. Road-projection-based eHMIs show promise in clarifying the specific yielding intention of an AV, although it may still not be an ideal solution. The findings point towards the need to consider the element of scalability early in the design process, and potentially the need to reconsider the current paradigm of eHMI design.

Earlier research [1] suggests that a social robot can contribute to gaining self-management skills for children with diabetes. In the current study, the positive effect of different robot characteristics of a social robot is investigated in a natural environment with end-users. The NAO robot played an interactive trivia game with eight children with diabetes, which was video-recorded. After the study children filled in a questionnaire and parents were interviewed. Combining the data provided several findings: movements of the robot were entertaining, not all children appreciated eye contact and expressions contributed to bonding. These results can be used to improve interactions between children and human-like robots.

In this paper, we investigate the effect of an external human-machine interface (eHMI) and a conspicuous external vehicle appearance due to visible sensors on pedestrian interactions with automated vehicles (AVs). Recent research shows that AVs may need to explicitly communicate with the environment due to the absence of a driver. Furthermore, in interaction situations, an AV that looks different and conspicuous owing to an extensive sensor system may potentially lead to hesitation stemming from mistrust in automation. Thus, we evaluated in a virtual reality study how pedestrian attitude, the presence/absence of an eHMI, and a conspicuous sensor system affect their willingness to cross the road. Results recommend the use of an eHMI. A conspicuous appearance of automated-driving capability had no effect for the sample as a whole, although it led to more efficient crossing decisions for those with a more negative attitude towards AVs. Our findings contribute towards the effective design of future AV interfaces.

A solid methodology to understand human perception and preferences in human-robot interaction (HRI) is crucial in designing real-world HRI. Social cognition posits that the dimensions Warmth and Competence are central and universal dimensions characterizing other humans [1]. The Robotic Social Attribute Scale (RoSAS) proposes items for those dimensions suitable for HRI and validated them in a visual observation study. In this paper we complement the validation by showing the usability of these dimensions in a behavior based, physical HRI study with a fully autonomous robot. We compare the findings with the popular Godspeed dimensions Animacy, Anthropomorphism, Likeability, Perceived Intelligence and Perceived Safety. We found that Warmth and Competence, among all RoSAS and Godspeed dimensions, are the most important predictors for human preferences between different robot behaviors. This predictive power holds even when there is no clear consensus preference or significant factor difference between conditions.

As robots are taking a more prominent role in our daily lives, it becomes increasingly important to consider how their presence influences us. Several studies have investigated effects of robot behavior on the extent to which that robot is positively evaluated. Likewise, studies have shown that the emotions a robot shows tend to be contagious: a happy robot makes us feel happy as well. It is unknown, however, whether the affect that people experience while interacting with a robot also influences their evaluation of the robot. This study aims to discover whether people's affective and evaluative responses to a social robot are related. Results show that affective responses and evaluations are related, and that these effects are strongest when a robot shows meaningful motions. These results are consistent with earlier findings in terms of how people evaluate social robots.

In recent years, much attention has been given to developing robots with various social skills. An important social skill is navigation in the presence of people. Earlier research has indicated preferred approach angles and stopping distances for a robot when approaching people who are interacting with each other. However, an experimental validation of user experiences with such a robot is largely missing. The current study investigates the shape and size of a shared interaction space and evaluations of a robot approaching from various angles. Results show an expected pattern of stopping distances, but only when a robot approaches the middle point between two persons. Additionally, more positive evaluations were found when a robot approached on the side of the participant compared to other participant's side. These findings highlight the importance of using a smart path planning method for robots when joining an interaction between users.

The near future will see an increasing demand of elder care and a shortage of professional and informal caregivers. In this context, ageing societies would benefit from the design of intelligent homes that provide assistance. The choice of interfaces between the assistive environment and the user is of great importance and determines the degree of user acceptance of this technology. Socially assistive robots are one of the most promising interfaces. Their embodiment and multimodal communication channels could potentially provide a large number of services that otherwise would have to be carried out by a variety of dedicated systems. Furthermore, evidence suggests that people perceive robots more as companions and social actors than tools and this is likely to steer user acceptance positively. This paper presents the authors' work related to the EU-FP7 project KSERA, a project that aims at introducing a socially assistive robot that acts as a proactive communication interface in smart home environments. In particular, it gives an overview of (1) human--robot interaction studies conducted in Eindhoven (The Netherlands) whose general aim was to preliminary assess the added value of socially assistive robots in intelligent homes and (2) the KSERA project field trials in Schwechat (Vienna) and Tel Aviv (Israel) that tested an integrated smart-home/robot system with real end users (N=16) in three real-world scenarios. Overall, results show that socially assistive robots positively affect user experience and motivation compared to standard smart environment interfaces such as touch screens. However, people still tend to prefer conventional interfaces for receiving information.

Humans are experts in cooperating in a smooth and proactive manner. Action and intention understanding are critical components of efficient joint action. In the context of the EU Integrated Project JAST [16] we have developed an anthropomorphic robot endowed with these cognitive capacities. This project and respective robot (ARoS) is the focus of the video. More specifically, the results illustrate crucial cognitive capacities for efficient and successful human-robot collaboration such as goal inference, error detection and anticipatory action selection. Results were considered one of the ICT “success stories”[22].

Gaze direction is an important communicative cue. In order to use this cue for human-robot interaction, software needs to be developed that enables the estimation of head pose. We began by designing an application that is able to make a good estimate of the head pose, and, contrary to earlier head pose estimation approaches, that works for non-optimal lighting conditions. Initial results show that our approach using multiple networks trained with differing datasets, gives a good estimate of head pose, and it works well in poor lighting conditions and with low-resolution images. We validated our head pose estimation method using a custom built database of images of human heads. The actual head poses were measured using a trakStar (Ascension Technologies) six-degrees-of-freedom sensor. The head pose estimation algorithm allows us to assess a person's focus of attention, which allows robots to react in a timely fashion to dynamic human communicative cues.

Embodied nonverbal cues are fundamental for regulating human-human social iteractions. The physical embodiment of robots makes it likely that they will have to exhibit appropriate nonverbal interactive behaviors. In this paper we propose a model of the user's proximity based on a superposition of quasi-Gaussian probability distributions which allows to express findings from HRI trials regarding distances and direction of approach in a human-robot interaction scenario. The way the model is formulated is suitable for well-established Bayesian filtering techniques, and thus the inference of the preferred distance and direction of approach in a human robot interaction scenario can be regarded as a state estimation problem. Results derived from simulations show the effectiveness of the inference process.