Robots that share daily environments with us are required to behave in a socially acceptable manner. There are two important approaches to this purpose: 1) robots model human behavior, understand it properly and behave appropriately 2) robots present their understanding and future behavior to surrounding people. In this paper, considering people present various cues to other people around them using gaze and face direction, we focus on the latter approach and propose a robot that presents cues to an opposing pedestrian by turning face. Another problem with the conventional research is that the evaluation of the pedestrian's ease of passing with the robot depends only on the subjective impression, so it was difficult to design the robot's behavior based on the temporal change of the ease of walking. In this paper, we evaluate the fluctuation of the pedestrian's moving velocity vector as an index of the ease of walking and analyze the temporal change. We have conducted preliminary experiments in which 12 subjects passed by the robot and compared the three types of presentation methods using the face. By presenting information using a face, we confirmed that the subjects tended to have better impressions of walking based on subjective evaluation and that the walking was relatively easy to walk for several seconds while approaching the robot based on analyzing the fluctuation of the moving speed vector.

Humans react to their surroundings based on their evaluation of the environment sensed by their five physical senses. We refer to this as the recognition-evaluation-action (REA) cycle. On another front, autonomous personal vehicles base their evaluation of the surrounding environment on information obtained through their sensors. This cycle is called the action cycle. In this study, we aim to identify an entrainment method between humans' REA cycle and autonomous personal vehicle action cycle, analyzing what occurs when these cycles correspond. In this study, we focus on entrainment between human environment evaluation and those of autonomous personal vehicles. This perspective has not been considered in previous studies; however its inclusion is essential to ensure that autonomous personal vehicles are not mere vehicles but a part of the passenger's body, as these become more common. In this study, we chose heart rate as the physiological index, because heart rate is affected by human environmental evaluations. We utilized the entrainment between human environmental evaluation and autonomous personal vehicle environmental evaluation as the method to associate heart rate with the action updating frequency. In addition, we conducted a study using our personal vehicle. In the results, a sigmoid function which showed that heart frequency was positively correlated with the action updating frequency; this shows that autonomous personal vehicles can operated according to their user's environmental evaluations.

This work proposes a Human-Comfortable Path Planner (HCoPP) system for autonomous passenger vehicles. The aim is to create a path planner that improves the feeling of comfort of the passenger, this topic is different from collision free planning and it has not received much attention. For this purpose, in addition to the shortest distance constraint conventionally used in path planning, constraints related to relevant environmental features are introduced. For straight segments, the constraint is based on the lane-circulation pattern preferred by humans. In curved segments and intersections, the constraint takes into account the visibility. A multi-layered cost map is proposed to integrate these additional constraints. To compute the human-comfortable path, a graph search algorithm was implemented. The evaluation of the proposed approach was conducted by having 30 participants riding an autonomous robotic wheelchair. The paths computed by the proposed path planner were compared towards a state of the art shortest-distance path planner implemented in the navigation stack of ROS. Experimental results show that the paths computed by the proposed approach are perceived as more comfortable.

This paper presents a study on intention communication in a navigational context using a robotic wheelchair. The robotic wheelchair uses light projection to communicate its motion intentions. The novelty of the work is threefold: the communication of robot intentions to the passenger, the consideration of passenger and robot as a group (“in-group”) [1] who share motion intentions and the communication of the in-group intentions to other pedestrians (the “out-group”). A comparison in an autonomous navigation task where the robotic wheelchair autonomously navigates the environment with and without intention communication was performed showing that passengers and walking people found intention communication intuitive and helpful for passing by actions. Evaluation results significantly show human participant preference for having navigational intention communication for the wheelchair passenger and the person passing by it. Quantitative results show the motion of the person passing by the wheelchair with intention communication was significantly smoother compared to without intention communication.

This work introduces a 3D visibility model for comfortable autonomous vehicles. The model computes a visibility index based on the pose of the wheelchair within the environment. We correlate this index with human navigational comfort (discomfort) and we discuss the importance of modeling visibility to improve human riding comfort. The proposed approach models the 3D visual field of view combined with a two-layered environmental representation. The field of view is modeled with information from the pose of the robot, a 3D laser sensor and a two-layered environmental representation composed of a 3D geometric map with traversale area information. Human navigational discomfort was extracted from participants riding the autonomous wheelchair. Results show that there is fair correlation between poor visibility locations (e.g., blind corners) and human discomfort. The approach can model places with identical traversable characteristics but different visibility and it differentiates visibility characteristics according to traveling direction.

Reliable autonomous navigation is an active research topic that has drawn the attention for decades, however, human factors such as navigational comfort has not received the same level of attention. This work proposes the concept of “comfortable map” and presents a navigation approach for autonomous passenger vehicles which in top of being safe and reliable is comfortable. In our approach we first extract information from users preference related to comfort while sitting on a robotic wheelchair under different conditions in an indoor corridor environment. Human-comfort factors are integrated to a geometric map generated by SLAM framework. Then a global planner computes a safe and comfortable path which is followed by the robotic wheelchair. Finally, an evaluation with 29 participants using a fully autonomous robotic wheelchair, showed that more than 90% of them found the proposed approach more comfortable than a shortest-path state of the art approach.

This paper proposes a wearable avatar named TEROOS, which is mounted on a person's shoulder. TEROOS allows the users who wear it and control it to share a vision remotely. Moreover, the avatar has an anthropomorphic face that enables the user who controls it to communicate with people co-located with the user who wears it. We have a field test by using TEROOS and observed that the wearable avatar innovatively assisted the users to communicate during their joint activities such as route navigating and buying goods at a shop. The user controlling TEROOS could give the user wearing it appropriate route instructions on the basis of the situation around TEROOS. In addition, both users could easily identify objects that they discussed. Moreover, shop staff members communicated with the user controlling TEROOS and behaved as they normally would when the user asked questions about the goods.

This paper presents the effect of a robot's active touch for improving people's motivation. For services in the education and healthcare fields, a robot might be useful for improving the motivation of performing such repetitive and monotonous tasks as exercising or taking medicine. Previous research demonstrated with a robot the effect of user touch on improving its impressions, but they did not clarify whether a robot's touch, especially an active touch, has enough influence on people's motive. We implemented an active touch behavior and experimentally investigated its effect on motivation. In the experiment, a robot requested participants to perform a monotonous task with a robot's active touch, a passive touch, or no touch. The result of experiment showed that an active touch by a robot increased the number of working actions and the amount of working time for the task. This suggests that a robot's active touch can support people to improve their motivation. We believe that a robot's active touch behavior is useful for such robot's services as education and healthcare.

This paper proposes a method that estimates customer interests by identifying a set of areas at which a customer has stopped in our experimental shop environment and recommends items through robots located on the shop's shelves. We experimentally verified that our method changed customer behaviors. The results show that the method successfully encouraged customers to visit the shelves of recommended items.

Social robots that provide services to humans in real environments have been developed in recent years. Such a robot should appropriately recognize its users' orders through human-like communications because of user-friendliness. However, their styles of communicating are too diverse to achieve this goal. If the robot could shape their styles, its recognition ability would be improved. An entrainment, which is a phenomenon where human's behavior is synchronized with robot's behavior, can be useful for this shaping. Previous studies have reported the entrainment occurring in the same modality, but they have given little attention to entrainment across different modalities (e.g., speech and gestures). We need to consider this cross-modal effect because human-robot interaction is inherently multi-modal. In this paper, we defined “mutual entrainment” as the entrainment across different modalities and investigated the effect of it through a laboratory experiment. We evaluate how the frequency of human pointing gestures varies with the amount of information in robot speech, and as a result, we found that the gesture frequency increased as the amount of information decreased. The results suggest that smoother human-robot communications can be achieved by shaping human behavior through mutual entrainment.

This paper reports the persuasion effect of a robot's whispering behavior that consists of a whispering gesture and a request made in a small voice. Whispering gestures naturally make close distance and create warmth feelings with subjects, and requests in quiet voices with whispering gestures also create familiar impressions, which are effective factors of persuasion. We believe that such physical behavior as whispering is one persuasion advantage held by real robots over computers. We conducted a between-subjects experiment to investigate the effectiveness of these two factors on persuasion. In the experiment, the robot requests an annoying task of the subjects; writing as many multiplication table equations as possible. As a result, whispering gestures significantly increased the working time and the number of equations. On the other hand, the loudness of the voice in the request had no effect. We believe the results indicate the effectiveness of physical behavior for persuasion in human-robot interaction.

We propose a portable robot named "Blog Robot" which presents blog contents by using verbal and non-verbal expression. Blog Robot is a robotized smart-phone which has a head and arms for making hand gestures, eye contact, and joint attention. The blog is widely used to express personal views or to record daily occurrences. One of the information frequently posted on the blog is related to a certain place such as a tourist site or a shop. Meanwhile, people sit down in front of their PC and check blogs through the text and the image displayed on the Web browser. However, their style of checking the blogs is not good way for them to realize the authentic situations which blog writers let them know. The user carries Blog Robot like cellular phone and can browse blogs related to the location where user is. The browse method makes the user access the blog at the real scene related to the contents of the blog. Blog Robot gives her/him the content of the blog by reading it with synthesized speech. In particular, the nonverbal information generated by Blog Robot enhances the read information as if the blog writer is next her/him while telling her/him it. The browse method is expected to enable the user to obtain more realistic information than the Web browser on the PC.

A communication robot must recognize a referred-to object to support us in daily life. However, using our wide human vocabulary, we often refer to objects in terms that are incomprehensible to the robot. This paper focuses on lexical entrainment to solve this problem. Lexical entrainment is the phenomenon of people tending to adopt the terms of their interlocutor. While this has been well studied in human-computer interaction, few published papers have approached it in human-robot interaction. To investigate how lexical entrainment occurs in human-robot interaction, we conduct experiments where people instruct the robot to move objects. Our results show that two types of lexical entrainment occur in human-robot interaction. We also discuss the effects of the state of objects on lexical entrainment. Finally, we developed a test bed system for recognizing a referred-to object on the basis of knowledge from our experiments.

In human-robot interaction, robots often fail to lead humans to intended reactions due to their limited ability to express affective nuances. In this paper, we propose a motion modification method that combines affective nuances with arbitrary motions of humanoid robots to induce humans to intended reactions by expressing affective states. The method is applicable to various humanoid robots that differ in degrees of freedom or appearances, and the affective nuances are parametrically expressed in a two-dimensional model comprised of valence and arousal. The experimental results showed that the desired affective nuances could be expressed by our method, but it also suggested some limitations. We believe that the method will contribute to interactive systems in which robots can communicate with appropriate expressions in various contexts.

In the near future, robots are expected to play active roles in human communities. After this time arrives, robots will need to be socially accepted by the people in the communities to which they belong. However, it remains unknown what issues must be resolved to make robots socially accepted. In this paper, we point out the three criteria with which people in communities evaluate a robot and three types of relations people have with the robot. We conducted a six-week study in an office to statistically test the relationship between the evaluations and how well a robot is accepted depending on relation types. We probe that by improving behaviors in the criteria the robot will be more accepted by each type of people. Then, the discussion are presented about the most important issues regarding the social acceptance of robots.

This paper investigates the effect on user impressions of the body direction of a stuffed doll robot in an animation system. Many systems that combine a computer display with a robot have been developed, and one of their applications is entertainment, for example, an animation system. In these systems, the robot, as a 3D agent, can be more effective than a 2D agent in helping the user enjoy the animation experience by using spatial characteristics, such as body direction, as a means of expression. The direction in which the robot faces, i.e., towards the human or towards the display, is investigated here.

This paper describes a method for recognizing the environment and selecting behaviors for a humanoid robot that are appropriate to the environment, based on sensor histories. In order for humanoid robots to move around many kinds of environments, one of the keys is selecting the appropriate behavior. Because features of an environment that deeply influence a robot's motion, such as viscous friction or floor elasticity, are difficult to immediately measure before moving, it is difficult for the robots to plan their behaviors to take these factors into account. Therefore, in our method, a robot first records a long time series of data from sensors attached to its body while performing each behavior in each environment, and it builds decision trees based on this data for each of the behaviors, enabling it to recognize the current environment based on the sensor history. By using the trees, the robot selects the behavior which will be most effective for recognizing the environment, acquires a sensor history while doing the behavior, and selects likely candidates for the environment. By iterating over these steps, the robot can recognize the environment and select an appropriate behavior. To verify the validity of the method, we used a small-size humanoid robot and conducted an experiment to recognize environments in a family's house

In face-to-face communication, eyes play a central role, for example in directing attention and regulating turn-taking. For this reason, it has been a central topic in several fields of interaction study. Although many psychology findings have encouraged previous work in both human-computer and human-robot interaction studies, so far there have been few explorations on how to move the agent's eye, including when to move it, for communication. Therefore, it is this topic we address in this study. The impression a person forms from an interaction is strongly influenced by the degree to which their partner's gaze direction correlates with their own. In this paper, we propose methods of controlling a robot's gaze responsively to its partner's gaze and confirm the effect of this on the feeling of being looked at, which is considered to be the basis of conveying impressions using gaze in face-to-face interaction experiments. Furthermore, an additional preliminary experiment with an on-screen agent shows the possibility of using blinking behaviour as another modality for responding to a partner

In face-to-face communication, eyes play a central role, for example, in directing attention and regulating turn-taking. For this reason, the eyes have been a central topic in several fields of interaction study. Although many psychological findings have encouraged previous work in both human-computer and human-robot interaction studies, so far there have been few explorations on how to move the agent's eye, including when to move it, for communication. Therefore, we investigate this topic in this study. The impression a person forms from an interaction is strongly influenced by the degree to which their partner's gaze direction correlates with their own. In this paper, we evaluate two primitive ways of controlling a robot's gaze responsively to its partner's gaze by comparison with two additional ways of non-responsive gaze control in an experiment set in a conversational situation. Consequently, we confirm that robots with different types of responsive gaze control can give subjects different impressions of its characteristics

This study clarified that a robot tracking a user can influence user decision-making. Two types of robot were used and the robots tracked or did not track the user. A laboratory experiment (n = 118) tested differences in selection ratios corresponding to a color name recommended by the robots. Subjects selected the recommended color name significantly more often when the robot tracked subjects' faces than in the no-recommendation situation, however, when the robot did not track subjects' faces with recommendation, there was no significant difference. The physiological data also showed that the tracking condition generated higher levels of arousal than the non-tracking condition. This suggests that tracking the user's face influences user decision-making.