Reo Matsumura

Effect of robot's active touch on people's motivation

This paper presents the effect of a robots active touch for improving peoples 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 robots touch, especially an active touch, has enough influence on peoples 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 robots 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 robots active touch can support people to improve their motivation. We believe that a robots active touch behavior is useful for such robots services as education and healthcare.

Does A Robot’s Touch Encourage Human Effort?

The paper investigated the effects on a person being touched by a robot to motivate her. Human science literature has shown that touches to others facilitate efforts of touched people. On the other hand, in the human–robot interaction research field, past research has failed to focus on the effects of such touches from robots to people. A few studies reported negative impressions from people, even if a touch from a person to a robot left a positive impression. To reveal whether robot touch positively affects humans, we conducted an experiment where a robot requested participants to perform a simple and monotonous task with/without touch interaction between a robot and participants. Our experiment’s result showed that both touches from the robot to the participants and touches from the participants to the robot facilitated their efforts.

Effect of Robot’s Whispering Behavior on People’s Motivation

This paper presents the effect of a robot’s whispering behavior on people’s motivation. Here, “whispering behavior” consists of a whispering cue and a small voice, which provides a natural sense of physical proximity in a context of confidentiality, thus increasing intimacy. A laboratory experiment was conducted to investigate this effect. In the experiment, a robot requested the participants to perform an annoying task that involved writing as many equations in a 9×9 multiplication table as possible. The result showed that the whispering cue improved task performance as measured by the number of written equations and writing time. The small voice, however, had no effect. Furthermore, to investigate the effectiveness of a robot’s whispering behavior on recommendations, we conducted a field trial in a shopping mall. The results showed the effectiveness of whispering on recommendations, suggesting that whispering behaviors are useful for various services that aim to build motivation, such as advertisements, sales promotions, and encouragement to study.

“Could i have a word?”: Effects of robot's whisper

This paper reports the persuasion effect of a robots 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.

Who is Interacting With me? Identification of an Interacting Person Through Playful Interaction With a Small Robot

Small robots are being designed to recognize behaviors through playful interaction. Prior work used data from impoverished sensing devices such as inertial sensors to analyze gestures and attitude in playful interaction through time series analysis. However, the prior work did not focus on individual differences required for person identification. This research hypothesizes that person identification can be achieved by determining individual differences in playful interaction by using inertial sensor data. We propose a method that iteratively narrows down the candidates during interaction to achieve accurate person identification. This method calculates the features using a time series of the inertial sensor data. These features identify a candidate who is playfully interacting with the robot using a decision tree classifier that includes combinations of the current candidates. The system stores the results as a dataset for voting, and the voting results are used to reduce the candidates until the number of candidates is winnowed to one. Evaluation results show that our proposed method identifies persons through playful interactions with 99.1% accuracy.

DEVELOPMENT OF A HIGH-PERFORMANCE HUMANOID SOCCER ROBOT

The RoboCup, which is a worldwide robot soccer competition, has set an ambitious goal for itself: to have a humanoid robot team win against human teams in World Cup Soccer by 2050. In order to achieve this goal, the robots require highly sophisticated sensory-data processing and decision-making functions. The development of robots for the RoboCup Humanoid League also has significant meaning for the development of robotics. However, this development is not easy and there are few papers covering it and its design policy. This paper reports the design policy for humanoids developed by Team Osaka, whose robots have been selected as the best humanoid robots four times in the last four years. In addition to the design policy, this paper also reports on the developmental process and comparisons among humanoid versions developed by Team Osaka. We believe that this paper will offer much information to other researchers who are developing humanoids for the RoboCup.

What kind of floor am I standing on? Floor surface identification by a small humanoid robot through full-body motions

This study addresses a floor identification method for small humanoid robots that work in such daily environments as homes. The fundamental difficulty lays in a method to understand the physical properties of floors. To achieve floor identification with small humanoid robots, we used inertial sensors that can be easily installed on such robots, and dynamically selected a full-body motion that physically senses floors to achieve accurate floor identification. We collected a training data-set over 10 different kinds of common floors in home environments. We achieved 85.7% precision with our proposed method. We also demonstrate that our robot could appropriately change its locomotion behaviours depending on the floor identification results.

Does an Animation Character Robot Increase Sales

This paper investigates whether a network robot salesclerk system increases sales in real shopping contexts. Our robot system, which consists of an autonomous virtual agent and a semi-autonomous physical agent, enables customers to interact with the virtual agents on their smartphones and reserve special character merchandise. Moreover, their virtual agent is transferred to the physical agent at the shop to physically distribute the reserved merchandise to customers. Through such cyber-physical interaction, we provided rich shopping experiences to customers to increase sales. We collaborated with an animation company, Production I.G Inc., and employed an animation character named Tachikoma from the Ghost in the Shell:Stand Alone Complex (a.k.a S.A.C. seriese) universe to design the appearance and the characteristics of both agents. We conducted field trials to investigate whether the developed system contributed to sales related to the animation merchandise of Ghost in the Shell, and the results showed our systems effectiveness.

Methods for Environment Recognition Based on Active Behaviour Selection and Simple Sensor History

The ability to operate in a variety of environments is an important topic in humanoid robotics research. One of the ultimate goals of this research is smooth operation in everyday environments. However, movement in a real-world environment such as a familys house is challenging because the viscous friction and elasticity of each floor, which directly influence the robots motion and are difficult to immediately measure, differ from place to place. There has been a lot of previous research into ways for the robots to recognize the environment. For instance, Fennema et al. (Fennema et al., 1987) and Yamamoto et al. (Yamamoto et al., 1999) proposed environment recognition methods based on range and visual information for wheeled robot navigation. Regarding humanoid robots, Kagami et al. (Kagami et al., 2003) proposed a method to generate motions for obstacle avoidance based on visual information. They measured features of the environment precisely before moving or fed back sensor information to a robots controller with a short sampling period. It is still difficult to measure the viscous friction or elasticity of the floor before moving or by using short term sampling data, and they did not deal with such features. Thus, we propose a method for recognizing the features of environments and selecting appropriate behaviours based on the histories of simple sensor outputs, in order to achieve a humanoid robot able to move around a house. Figure 1 shows how our research differs from previous research according to length of the sensor history and number of types of sensors. The key idea of our method is to use a long sensor history to determine the features of the environment. To measure such features, almost all previous research (Shats et al., 1991; Holweg et al., 1996) proposed methods that used several kinds of sensors with a large amount of calculations to quickly process the sensor outputs. However, such approaches are unreasonable because the robot lacks sufficient space on its body for the attached sensors and processors. Hence we propose using sensor history to measure them because there are close relationships between sensor histories, motions, and environments. When the robot performs specific motions in specific environments, we can see those features in the sensor history that describe the motion and the environment. Furthermore, such features as viscous friction or floor elasticity do not change quickly. Thus we can use a long history of sensor data to measure them.