Designing policies that are both efficient and acceptable for conversational service robots in open and diverse environments is non-trivial. Unlike fixed, hand-tuned parameters, online learning can adapt to non-stationary conditions. In this paper, we study how to adapt a social robot’s speech policy in the wild. During a 12-day in-situ deployment with over 1,400 public encounters, we cast online policy optimization as a multi-armed bandit problem and use Thompson sampling to select among six actions defined by speech rate (slow/normal/fast) and verbosity (concise/detailed). We compare three complementary binary rewards–Ru (user rating), Rc (conversation closure), and Rt (≥2 turns)–and show that each induces distinct arm distributions and interaction behaviors. We complement the online results with offline evaluations that analyze contextual factors (e.g., crowd level, group size) using video-annotated data. Taken together, we distill ready-to-use design lessons for deploying online optimization of speech policies in real public HRI settings.

We report a mixed-methods field experiment of a conversational service robot deployed under everyday staffing discretion in a live bedding store. Over 12 days we alternated three conditions--Baseline (no robot), Robot-only, and Robot+Fixture--and video-annotated the service funnel from passersby to purchase. An explanatory sequential design then used six post-experiment staff interviews to interpret the quantitative patterns.

Voice is an essential modality for human-robot interaction (HRI). The way a robot sounds plays a central role in shaping how humans perceive and engage with it, influencing factors such as intelligibility, understandability, and likability. Although prior work has examined voice design, most studies occur in controlled labs, leaving uncertainty about how results translate to real-world settings. To address this gap, we conducted two naturalistic deployment studies with a guidance robot in a shopping mall: (1) in-depth interviews with six participants, and (2) an eight-day field deployment using a 3×3 design varying speech rate and volume, yielding 725 survey responses. Our results show how real-world context shapes voice perception and inform adaptive, context-aware voice design for social robots in public spaces.

Service robots have increasingly been investigated in retailing. Previous studies mainly focused on the effectiveness of recommendation with regard to a single robot, and whether and how the use of two robots combined can achieve better performance remain unclear. In this study, we address this by exploring the combination power of two service robots for product recommendation in a bakery. We placed one robot inside the store for product recommendation and the other robot outside to promote the inside robot. Particularly, we are interested in the effects of the outside robot on the inside robot's performance in product recommendation. Our results indicate that using the outside robot to promote the inside robot achieved more purchases over using the inside robot alone. Particularly, we discovered that the outside robot increased the attention of customers toward the inside robot; hence, more customers checked and purchased the products. Based on the findings, we discuss the important points for the effective use of service robots.

In this study, we explore the effective form of social presence for a tele-operated robot to provide customer service. Particularly, we address the question on if and how a tele-operated robot displays the presence of an operator, and what effects it would have on people's perception and behavior toward it. We launched a tele-operated robot in a supermarket and had it deliver recipe flyers. We adjusted the robot's social presence by showing or not showing the photo of an operator's face (F) and using or not using voice conversion (V), leading to three forms of presence: Wizard of Oz (F: no, V: yes), costume (F: yes, V: yes), and telepresence (F: yes, V: no), which indicated the operator's presence from a low to a high level. We determined that the customers behaved significantly different when they faced the tele-operated robot in different forms. Our robot that exhibited a moderate presence of the operator (costume form) achieved the overall best performance. Based on these findings, we discuss both the strengths and weaknesses of the three forms of presence for a tele-operated robot and recommend the appropriate form for various applications.

Customer service with teleoperated robots is suscep-tible to the same problems related to stress as is emotional labor in general, such as for in-person customer service representatives. In this study, we aimed to reduce that stress by constructing a buddy-like rapport between the robot, which is the target of teleoperation, and its operator. For this purpose, we designed an empathetic interaction between the robot and the operator and conducted a customer service experiment to verify its effectiveness. Our results demonstrate that the proposed interaction can build rapport between the robot and the operator and the operator can feel more reassured. Although the effect on stress could not be isolated directly from the data, detailed analyses of the response to the questionnaires indicated that the proposed interaction may be useful to relieve stress.

In this paper, we report on a field study in which we employed two service robots in a bakery store as a sales promotion. Previous studies have explored public applications of service robots public such as shopping malls. However, more evidence is needed that service robots can contribute to sales in real stores. Moreover, the behaviors of customers and service robots in the context of sales promotions have not been examined well. Hence, the types of robot behavior that can be considered effective and the customers' responses to these robots remain unclear. To address these issues, we installed two tele-operated service robots in a bakery store for nearly 2 weeks, one at the entrance as a greeter and the other one inside the store to recommend products. The results show a dramatic increase in sales during the days when the robots were applied. Furthermore, we annotated the video recordings of both the robots' and customers' behavior. We found that although the robot placed at the entrance successfully attracted the interest of the passersby, no apparent increase in the number of customers visiting the store was observed. However, we confirmed that the recommendations of the robot operating inside the store did have a positive impact. We discuss our findings in detail and provide both theoretical and practical recommendations for future research and applications.

Social robotics recommendations have been studied for a long time, and many existing studies have addressed in-store recommendations using robots. However, it has been pointed out that many studies conducted in "in-the-wild" field environments have only focused on the initial stages of customer purchase behavior, such as stopping and engaging in a conversation, and few have been able to induce an interest in the product and even purchase. One of the causes is that the robot itself attracts most of the customer’s attention, making it difficult for customers to be interested in the robot’s recommendations. To solve this problem, this study examines the inclusion of clear and specific instructions to customers in interactions in which the robot recommends services and products. We conducted a field experiment to confirm the effectiveness of such instructive interactions, and found that customers are more likely to be interested in the content recommended by the robot, rather than in the robot itself, through the instructive interaction.

To explore the interaction design of an autonomous social robot stationed in a hotel room, we conducted a Wizard of Oz study. We developed a teleoperated robotic system that appears to move autonomously through voice-to-synthesis processing. Comparing the evaluation of the latest autonomous case with one of these teleoperated cases, the results show that it is possible to construct a robotic system that is more highly rated in terms of warmth, competence, and enjoyment of conversation. The results also suggest novel forms of the hotel room robot’s interactive services, such as a hotel-life management service and conversation partner service as a role of a listening presence, which draws out and understands with the guests’ talk.

In this paper, we explore how a utility robot might express emotions via expressive lights and in-situ motions. In most previous work, methods for either modality were investigated alone, leaving a huge potential to improve the expression of emotions by combining the two modalities. We present a series of three studies, one for investigating how well people might recognize emotions on the basis of expressive light cues alone, one for exploring how people might perceive affect towards in-situ motion characteristics, and one for further combining the two modalities and studying whether multi-modal expressions could be better recognized by people. Results from the first study show participants were not able to recognize target emotions with high accuracy. Results from the second suggest a relationship between the in-situ motion characteristics of a robot and perceived affect. Results from the third suggest that expressions that combine in-situ motions with expressive lights were better able to convey many emotions but not all. We conclude that adding in-situ motions to affective expressive lights appears to be better able to help convey emotions. These findings are important for designing affective behaviors for future utility robots that need to possess certain social abilities.

Bioluminescence is the production and emission of light by a living organism. It, as a means of communication, is of importance for the survival of various creatures. Inspired by bioluminescent light behaviors, we explore the design of expressive lights and evaluate the effect of such expressions on a human»s perception of and attitude toward an appearance-constrained robot. Such robots are in urgent need of finding effective ways to present themselves and communicate their intentions due to a lack of social expressivity. We particularly focus on the expression of attractiveness and hostility because a robot would need to be able to attract or keep away human users in practical human-robot interaction (HRI) scenarios. In this work, we installed an LED lighting system on a Roomba robot and conducted a series of two experiments. We first worked through a structured approach to determine the best light expression designs for the robot to show attractiveness and hostility. This resulted in four recommended light expressions. Further, we performed a verification study to examine the effectiveness of such light expressions in a typical HRI context. On the basis of the findings, we offer design guidelines for expressive lights that HRI researchers and practitioners could readily employ.

Functional robots are generally restricted in appearance, thus lacking ways to express their intent. In human-human interaction, gaze is an important cue for providing information and regulating interaction. In this pilot study, we investigate how we can implement gaze behavior in functional robots since gaze communication can allow humans to read a robot's intent and adjust their behavior accordingly. We explore design principles based on LED lights as we consider LEDs to be easily installed in most robots while not introducing features that are too human-like (to prevent users from having high expectations). In the paper, we present a design interface that allows designers to explore the parameter space of an LED strip attached to a Roomba robot. We then summarize a set of design principles for optimally simulating light-based gazes. Finally, our suggested design is evaluated by a large group of participants, and their comments are discussed.

Expressive light has been explored in a handful of previous studies as a means for robots, especially appearance- constrained robots that are not able to employ human-like expressions, to convey internal states and interact with people. However, it is still unknown how different light expressions can affect a person's perception and behavior. In this poster, we explore this research question by studying the effects of different expressive light animations on people's intention to buy hedonic products and how they choose between healthy and unhealthy food. Our preliminary results show that participants assigned to a positive and low arousal light animation condition had a higher intention of purchasing hedonic products and were inclined to choose unhealthy over healthy food. Such findings are in line with previous literature in marketing research, suggesting that mental alertness mediates the effect of external stimuli on a person's behavioral intentions. Future work is thus required to evaluate such findings in a human-robot interaction context.

Many researchers are now dedicating their efforts to studying interactive modalities such as facial expressions, natural language, and gestures. This phenomenon makes communication between robots and individuals become more natural. However, many robots currently in use are appearance constrained and not able to perform facial expressions and gestures. In addition, although humanoid-oriented techniques are promising, they are time and cost consuming, which leads to many technical difficulties in most research studies. To increase interactive efficiency and decrease costs, we alternatively focus on three interaction modalities and their combinations, namely color, sound, and vibration. We conduct a structured study to evaluate the effects of the three modalities on a human's emotional perception towards our simple-shaped robot "Maru." Our findings offer insights into human-robot affective interactions, which can be particularly useful for appearance-constrained social robots. The contribution of this work is not so much the explicit parameter settings but rather deepening the understanding of how to express emotions through the simple modalities of color, sound, and vibration while providing a set of recommended expressions that HRI researchers and practitioners could readily employ.

A preliminary experiment is carried out to investigate the effects of LED light animations on a user's perception of a computer. As anthropomorphism has become an important factor in interaction design, current research tends to add human-like expression abilities to interactive devices. Such methods, however, have limitations as they are complex and not applicable to many currently-in-use appearance-constrained devices such as personal computers. Thus, in this work we investigate an alternative method: expressive light. We attached a programmable RGB LED strip to the front-bottom of a monitor and developed a ping pong game for carrying out an experiment. We collected both game log and questionnaire data from participants. Our results show that participants who played the game with LED light animations liked the game more and perceived the computer as better and more humanlike. In addition, no evidence suggested a negative effect on a user's task performance or lead to additional workload.

As robotics has advanced, research on conveying a robot's emotional state to a person has become a hot topic. Most current studies are focused on interaction modalities such as facial expressions and natural language. Although many of the results seem to be promising, they suffer from high cost and technical difficulties. In this paper, we turn our attention to three other interaction modalities: color, sound, and vibration. Such modalities have the advantage of being simple, low cost, and intuitive. We conducted a pilot study to evaluate the effects of the three modalities on a human's emotional perception towards our robot Maru. Our result indicates that humans tend to interpret a robot's emotion as negative (angry in particular) when vibration and sound are used, while they interpret the emotion as relaxed when only color modality is used. In addition, the participants showed preference towards the robot when using all three modalities.

In this paper, we introduce a novel gesture recognition algorithm named the ant learning algorithm (ALA), which aims at eliminating some of the limitations with the current leading algorithms, especially Hidden Markov Models. It requires minimal training instances and greatly reduces the computational overhead required by both training and classification. ALA takes advantage of the pheromone mechanism from ant colony optimization. It uses pheromone tables to represent gestures, which scales well with gesture complexity. Our experimental results show that ALA can achieve a high recognition accuracy of 91.3% with only one training instance, and exhibits good generalization.