This study examines whether the humanoid robot, HuGo, can effectively influence human behavior in routine scenarios, using receipt collection at a university cafeteria as a case study. The robot encourages customers to remember to take their receipts at the self-checkout register, as negligence to do so creates additional workload for cafeteria employees. The research tests whether using verbal and pointing gestures enhances compliance compared to verbal-only or text-based prompts. The testing is conducted as an in-the-wild experiment, and the data is analyzed with both Chi-squared test and Two-Proportion Z-test. Results indicate that introducing speech makes a significant improvement in user compliance, while the influence of hand gestures is inconclusive.

Today's social robots' acceptance is limited by high maintenance costs, limited adaptability, and environmental waste among other factors. We propose that a modular humanoid platform design, that enables easy customization, upgrades, and repairs will diminish the above impediments and thus contribute to more widespread adoption of these devices. Accordingly, we set out to design and build a platform called HuGo, which relies on high-powered smart servo motors, single-board computers, and reusable PLA components as essential and reusable building blocks. This robot can support diverse applications, for example in healthcare, education, customer service, and entertainment. The modules of HuGo, such as arms, head, and torso, are interchangeable for easy reconfiguration for particular tasks, allowing sustainability and accessibility. Continuous testing at hospitals, universities, and in research projects confirms that the design is valid and reliable. Plans involve expanding modularity for stand-alone head modules, increasing their versatility to broader use cases.

Service robots are becoming a common sight in hospital corridors in many developed countries, but their acceptance, the factors influencing it, and potential cultural differences between countries remain unclear. To investigate this, the authors examined the use of service robots in two hospitals, one in Denmark and the other in South Korea, through an exploratory study. The analysis was performed based on retrospective data including usage parameters and a survey was conducted to assess the attitudes of healthcare personnel toward blood sample and medication delivery robots. The survey found that the staff in both hospitals were positive regarding the deployment of such robots. Additionally, the results indicate that the individualization of robots may lead to higher agency and that swift removal of technical issues at deployment might positively influence longer-term satisfaction with the robots.

In interaction with robots, verbal interaction is important and can have an impact on the perception of the robot. Experiences from a pilot study showed that the KUBO robot was not intuitive to use. To investigate if tailored verbal utterances could make the robot more intuitive to use, a new usability test combined with a Wizard of Oz method, where a facilitator played verbal utterances when KUBO drove over the specific TagTiles, was carried out. The test shows that the tailored verbal utterances helped in the understanding of KUBO and the actions of the robot when passing over the TagTiles which therefore made it more intuitive to use. Even though the verbal utterances helped the users, there were still some TagTiles they did not understand. The test also showed that there was a considerable mismatch between what the participants stated they understood and what was observed they really understood.

This paper compares how much people trust in a robotic system and in a group of people. Our experiment focuses on trust in robot knowledge versus trust in collective human knowledge tested with the help of a quiz game. During the experiments, people are competing with each other and gain or lose points based on their decisions. A joker system was designed that provides evidence on whether people rely rather on the robot or on human collective knowledge. In this setup, perceived intelligence is therefore highly correlated with trustworthiness. In the scope of this joker-picking system, overall results show that the trust in the robotic system is higher than the trust in collective human knowledge.

Our everyday living environment is created for people. When mobile robots negotiate this space, they might get stuck on hard to solve obstacles thus requiring people's help. The robot will need to persuade passers-by to assist it in overcoming these obstacles to reduce the need for maintenance interventions, which can be costly. In our study, we enabled a mobile robot to communicate its need for assistance in multiple ways, including beeping, synthesized voice and movement. These behaviors were tested in combination, to ascertain which is the most effective. We found that the robot which communicates being stuck using movement and voice gets the most help, while static, beeping and silent robots get much less help. However, subjective data shows that people might feel even more empathy towards the robot expressing its problem by beeping compared to using spoken messages.

Robot navigation in environments shared with humans should take into account social structures and interactions. The identification of social groups has been a challenge for robotics as it encompasses a number of disciplines. We propose a hierarchical clustering method for grouping individuals into free standing conversational groups (FSCS), utilising their position and orientation. The proposed method is evaluated on the SALSA dataset with achieved F1 score of 0.94. The algorithm is also evaluated for scalability and implemented on a mobile robot attempting to detect social groups and engage in interaction.

In this paper, we present a method for proactive approaching of humans for human-robot cooperative tasks such as a robot serving beverages to people. The proposed method can deal robustly with the uncertainties in the robot’s perception while also ensuring socially acceptable behavior. We use multiple modalities in the form of the robot’s motion, body orientation, speech and gaze to proactively approach humans. Further, we present a behavior tree based control architecture to efficiently integrate these different modalities. The proposed method was successfully integrated and tested on a beverage serving robot. We present the findings of our experiments and discuss possible extensions to address limitations.

Hand disinfection in public spaces is of great importance in preventing infectious diseases. However not everyone sanitizes their hands using hand sanitizer dispensers, and even if they do, many of them don’t rub hands for a long enough time for the sanitizing agent to become most effective. For these reasons we designed a robotic interface for automatic hand sanitizer dispensers to motivate people to disinfect their hands more often and for a longer time. We use interactive elements like speech and gaze communication to achieve this. In our in-the-wild studies we have found that using our system resulted in 21% more hand sanitizations and a much longer hand rubbing time, which in turn leads to better public hygiene and better prevention of infectious diseases.

Hiroshi Ishiguro gave a lecture to a group of young students. We employed CommU, the desktop social robot, to manage the questions and answers for the talk. We encouraged the students to ask questions anytime. Half of the classroom was told to ask questions by raising their hand while the other half was shown an online messaging system developed for CommU, which allows the audience to post questions, which the robot would directly say. We had a gatekeeper to monitor for invalid sentences. In the middle of the presentation we asked the students to shift roles. The robot used a neural network based estimator of interruptibility to find the best time to speak. We did not expect too many questions, but the audience really embraced using the robot. They posted 44 questions to the presenter through CommU. On the other hand they asked 8 direct questions by raising their hands and standing up. Students thought that they gained more information from the lecturer using the robot than using the conventional method. In this instance we didnt stop the students from asking too many questions, but in a real-world application the gatekeeper will have to play an important role.

In recent decades and years classroom teaching has been influenced by the appearance of digital devices and the pervasiveness of the internet. In recent years robots started appearing in learning environments. Even though classroom presentations improved considerably, interaction between students and teachers has not improved greatly. We propose a new way of using a desktop robot and an online voting/messaging system to make the classroom experience more interactive. Concretely, in this paper we report on a lecture which was assisted by a robot. The students posted questions and opinions on an online messaging platform. The messages were then sent to the robot to be posed to the teacher, either directly or through a voting procedure. Students found this new way of interaction very efficient and useful for improving their communication with the teacher. At the same time the number of interactions should be controlled so it does not interfere with the teaching process.

Humans use eye gaze in their daily interaction with other humans. Humanoid robots, on the other hand, have not yet taken full advantage of this form of implicit communication. We designed a passive monocular gaze tracking system implemented on the iCub humanoid robot [Metta et al. 2008]. The validation of the system proved that it is a viable low-cost, calibration-free gaze tracking solution for humanoid platforms, with a mean absolute error of about 5 degrees on horizontal angle estimates. We also demonstrated the applicability of our system to human-robot collaborative tasks, showing that the eye gaze reading ability can enable successful implicit communication between humans and the robot.

Robots are at the position to become our everyday companions in the near future. Still, many hurdles need to be cleared to achieve this goal. One of them is the fact that robots are still not able to perceive some important communication cues naturally used by humans, e.g. gaze. In the recent past, eye gaze in robot perception was substituted by its proxy, head orientation. Such an approach is still adopted in many applications today. In this paper we introduce performance improvements to an eye tracking system we previously developed and use it to explore if this approximation is appropriate. More precisely, we compare the impact of the use of eye- or head-based gaze estimation in a human robot interaction experiment with the iCub robot and naïve subjects. We find that the possibility to exploit the richer information carried by eye gaze has a significant impact on the interaction. As a result, our eye tracking system allows for a more efficient human-robot collaboration than a comparable head tracking approach, according to both quantitative measures and subjective evaluation by the human participants.

It is generally accepted that a robot should exhibit a contingent behavior, adaptable to the needs of each individual user, to achieve a more natural and pleasant interaction. In this paper we have evaluated whether this general rule applies also when the robot plays a leading role and needs to motivate the human partner to keep a certain pace, as during training or teaching. Also among humans, in schools or factories, structured interaction is often guided by a predefined rhythm, which facilitates the coordination of the partners involved and is thought to maximize their efficiency. On the other hand, a pre-established timing forces all participants to adjust their natural speed to the external, sometimes not appropriate, timing requirement. Where does the optimal trade-off between these two paradigms lie? We have addressed this question in a dictation scenario where the humanoid robot iCub plays the role of a teacher and dictates brief English or Italian sentences to the participants. In particular we compare a condition in which the dictation is performed at a fixed timing with a condition in which iCub monitors subjects' gaze to adjust its dictation speed. The results are discussed both in terms of participants' subjective evaluation and their objective performance, by highlighting the advantages and drawbacks of the choice of contingent robot behavior.

Passing an object between two humans is a very natural and seamless operation, mainly thanks to non-verbal cues which facilitate the process. Just from action observation, humans can easily anticipate where and when a passing movement will end and how heavy the transported object is. But how could this natural understanding be ported to non-human agents? We introduce a simple robotic architecture to enable the iCub humanoid robot to visually recognize the weight of an object and select a lift-to-pass motion which implicitly communicates such information to the action partner. In this work we mainly focus on the building and training of the procedural memory module needed to store the association between the mass of an object and its visual appearance, and we propose how such a model can be used to successively select communicative lifting motions.

Knowing where a person is looking is an important parameter of every human-human interaction. Detecting a person's gaze could significantly improve the interaction capabilities of today's robotic agents. But many robots' visual systems are limited by data bandwidth and optical hardware. We propose a low-cost high-def pan/tilt/zoom active vision system that could significantly improve the robot's eye tracking capabilities. We tested the proposed system for improving mutual gaze detection in a human-robot interaction scenario and found significant results compared to systems without zoom capability.

We explore the feasibility of using pupil diameter to estimate how the cognitive load of the driver changes during a spoken dialogue task with a remote conversant. The conversants play a series of Taboo games, which do not follow a structured turn-taking nor initiative protocol. We contrast the driver's pupil diameter when the remote conversant begins speaking with the diameter right before the driver responds. Although we find a significant difference in pupil diameter for the first pair in each game, subsequent pairs show little difference. We speculate that this is due to the less structured nature of the task, where there are no set time boundaries on when the conversants work on the task. This suggests that spoken dialogue systems for in-car use might better manage the driver's cognitive load by using a more structured interaction, such as system-initiative dialogues.

Augmented reality (AR) navigation systems are likely to improve the driving experience compared to today's personal navigation devices on the dashboard, as they don't require glances away from the road ahead. As technology is not yet capable to deliver an affordable and seamless HUD AR solution, we explore an inexpensive version of augmentation, which would have a similar benefit of reduced distraction. We propose using an LED (light emitting diode) matrix in the periphery of the driver's vision to indicate turns on the road. We find that such a system produces better results in visual attention, driving performance and in subjective measures compared to standard navigation devices.

In a driving simulator study, we explore the feasibility of using pupil diameter to estimate how the cognitive load of the driver changes during a spoken dialogue with a remote conversant. We confirm that it is feasible to use pupil diameter to differentiate between parts of the dialogue that increase the cognitive load of the driver, and those that decrease it. Our long term goal is to build a spoken dialogue system that can adapt its behavior when the driver is under high cognitive load, whether from the driving task or the dialogue task. Index Terms: dialog, cognitive load, pupil diameter, driving 1.

In driving simulator studies pupil diameter is often employed as a physiological measure of cognitive load. However, pupil size is primarily influenced by the pupillary light reflex (PLR). In this paper, we explore the influence of the size and luminance of visual targets on the PLR. Our results indicate that even for small targets (angular radius of 2.5°) changes in luminance can result in PLR that can obscure cognitive load-related pupil diameter changes. We propose a weighting function to be used to predict the PLR and present initial results that support its utility.

Pupil diameter is an important measure of cognitive load. However, pupil diameter is also influenced by the amount of light reaching the retina. In this study we explore the interaction between these two effects in a simulated driving environment. Our results indicate that it is possible to separate the effects of illumination and visual cognitive load on pupil diameter, at least in certain situations.

Prior research has shown that when drivers look away from the road to view a personal navigation device (PND), driving performance is affected. To keep visual attention on the road, an augmented reality (AR) PND using a heads-up display could overlay a navigation route. In this paper, we compare the AR PND, a technology that does not currently exist but can be simulated, with two PND technologies that are popular today: an egocentric street view PND and the standard map-based PND. Using a high-fidelity driving simulator, we examine the effect of all three PNDs on driving performance in a city traffic environment where constant, alert attention is required. Based on both objective and subjective measures, experimental results show that the AR PND exhibits the least negative impact on driving. We discuss the implications of these findings on PND design as well as methods for potential improvement.

We report on the results of a study in which pairs of subjects were involved in spoken dialogues and one of the subjects also operat-ed a simulated vehicle. We estimated the driver’s cognitive load based on pupil size measurements from a remote eye tracker. We compared the cognitive load estimates based on the physiological pupillometric data and driving performance data. The physiologi-cal and performance measures show high correspondence suggest-ing that remote eye tracking might provide reliable driver cogni-tive load estimation, especially in simulators. We also introduced a new pupillometric cognitive load measure that shows promise in tracking cognitive load changes on time scales of several seconds.

Nowadays, personal navigation devices (PNDs) that provide GPS-based directions are widespread in vehicles. These devices typically display the real-time location of the vehicle on a map and play spoken prompts when drivers need to turn. While such devices are less distracting than paper directions, their graphical display may distract users from their primary task of driving. In experiments conducted with a high fidelity driving simulator, we found that drivers using a navigation system with a graphical display indeed spent less time looking at the road compared to those using a navigation system with spoken directions only. Furthermore, glancing at the display was correlated with higher variance in driving performance measures. We discuss the implications of these findings on PND design for vehicles.