Physical agents that can autonomously generate engaging, life-like behaviour will lead to more responsive and interesting robots and other autonomous systems. Although many advances have been made for one-to-one interactions in well controlled settings, future physical agents should be capable of interacting with humans in natural settings, including group interaction. In order to generate engaging behaviours, the autonomous system must first be able to estimate its human partners' engagement level. In this paper, we propose an approach for estimating engagement from behaviour and use the measure within a reinforcement learning framework to learn engaging interactive behaviours. The proposed approach is implemented in an interactive sculptural system in a museum setting. We compare the learning system to a baseline using pre-scripted interactive behaviours. Analysis based on sensory data and survey data shows that adaptable behaviours within a perceivable and understandable range can achieve higher engagement and likeability.
Interactions in natural settings, e.g., public spaces, are too complex to be simulated in controlled laboratory settings, so to understand natural HRI it is necessary to conduct field studies. Many human-robot interaction systems have been tested in public spaces such as hospitals @cite_18 , train stations @cite_4 , service points @cite_17 , airports @cite_31 , shopping malls @cite_12 , hotels @cite_5 and museums @cite_24 , among others. Although robots investigated in these papers are sometimes surrounded by a group of people in a public place, these robots typically interact with only one person at a specific time, i.e., one-to-one interaction. Unlike these works, the larger scale of LAS enables LAS to simultaneously accommodate interactions with multiple people. In addition, the behaviours of robots studied in most of these works are pre-designed by researchers without continuous learning and adaptability.
@cite_18 @cite_4 @cite_24 @cite_5 @cite_31 @cite_12 @cite_17
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Robots are becoming increasingly integrated into the workplace, impacting organizational structures and processes, and affecting products and services created by these organizations. While robots promise significant benefits to organizations, their introduction poses a variety of design challenges. In this paper, we use ethnographic data collected at a hospital using an autonomous delivery robot to examine how organizational factors affect the way its members respond to robots and the changes engendered by their use. Our analysis uncovered dramatic differences between the medical and post-partum units in how people integrated the robot into their workflow and their perceptions of and interactions with it. Different patient profiles in these units led to differences in workflow, goals, social dynamics, and the use of the physical environment. In medical units, low tolerance for interruptions, a discrepancy between the perceived cost and benefits of using the robot, and breakdowns due to high traffic and clutter in the robot's path caused the robot to have a negative impact on the workflow and staff resistance. On the contrary, post-partum units integrated the robot into their workflow and social context. Based on our findings, we provide design guidelines for the development of robots for organizations. This paper reports a method that uses humanoid robots as a communication medium. There are many interactive robots under development, but due to their limited perception, their interactivity is still far poorer than that of humans. Our approach in this paper is to limit robots' purpose to a non-interactive medium and to look for a way to attract people's interest in the information that robots convey. We propose using robots as a passive-social medium, in which multiple robots converse with each other. We conducted a field experiment at a train station for eight days to investigate the effects of a passive-social medium. This paper reports on a field trial with interactive humanoid robots at a science museum where visitors are supposed to study and develop an interest in science. In the trial, each visitor wore an RFID tag while looking around the museum's exhibits. Information obtained from the RFID tags was used to direct the robots' interaction with the visitors. The robots autonomously interacted with visitors via gestures and utterances resembling the free play of children [1]. In addition, they performed exhibit-guiding by moving around several exhibits and explaining the exhibits based on sensor information. The robots were highly evaluated by visitors during the two-month trial. Moreover, we conducted an experiment in the field trial to compare the detailed effects of exhibit-guiding and free-play interaction under three operating conditions. This revealed that the combination of the free-play interaction and exhibit-guiding positively affected visitors' experiences at the science museum. This paper presents four exploratory studies of the potential use of robots for gathering customer feedback in the hospitality industry. To account for the viewpoints of both hotels and guests, we administered need finding interviews at five hotels and an online survey concerning hotel guest experiences with 60 participants. We then conducted the two deployment studies based on deploying software prototypes for Savioke Relay robots we designed to collect customer feedback: (i) a hotel deployment study (three hotels over three months) to explore the feasibility of robot use for gathering customer feedback as well as issues such deployment might pose and (ii) a hotel kitchen deployment study (at Savioke headquarters over three weeks) to explore the role of different robot behaviors (mobility and social attributes) in gathering feedback and understand the customers' thought process in the context that they experience a service. We found that hotels want to collect customer feedback in real-time to disseminate positive feedback immediately and to respond to unhappy customers while they are still on-site. Guests want to inform the hotel staff about their experiences without compromising their convenience and privacy. We also found that the robot users, e.g. hotel staff, use their domain knowledge to increase the response rate to customer feedback surveys at the hotels. Finally, environmental factors, such as robot's location in the building influenced customer response rates more than altering the behaviors of the robot collecting the feedback. In order to be successful, guide robots in public space require socially-intelligent navigation behaviors. Evaluation of these behaviors can be done through lab studies, though these do not always capture the complexities of interactions in "the wild". In this extended abstract we present initial results of a field trial of a multi-year project in which we developed and deployed a robot which provided guiding services to real passengers at one of the top-20 busiest airports in the world. During this field trial 9 groups of passengers were guided by the robot. We will present initial results and implications for field studies. This paper reports our research on developing a robot that distributes flyers to pedestrians. The difficulty is that since the potential receivers are pedestrians who are not necessarily cooperative, the robot needs to appropriately plan its motions, making it easy and non-obstructive for the potential receivers to accept the flyers. We analyzed peoples distributing behavior in a real shopping mall and found that successful distributors approach pedestrians from the front and only extend their arms near the target pedestrian. We also found that pedestrians tend to accept flyers if previous pedestrians took them. Based on these analyses, we developed a behavior model of the robots behavior, implemented it in a humanoid robot, and confirmed its effectiveness in a field experiment. Social robots are emerging as potentially useful tools for customer service in various contexts including retail, healthcare or education. Their capacity for human-like interaction could provide a satisfactory customer experience in simple and repetitive tasks, while allowing human staff to focus on issues that are more complex. In this paper, we report the initial findings of a two-day field study of using a social robot Pepper for guidance and edutainment at the service point of a medium-sized city in Finland. We collected observations and semi-structured interviews of altogether 89 customers visiting the service point. Fifteen specific experiences evoked by the interaction with the robot were identified and categorized under five basic needs of Autonomy, Competence, Relatedness, Stimulation and Security. We discuss implications for an experience-driven design process of applications for social service robots.