There are many interactive design tools: Adobe Photoshop, GIMP, and so on. The interactive design tools often provide their users many visual effects. The users need to look for design parameter values appropriate to their desired visual effect. Interactive design can be dealt as a sort of optimal solution search problems. In optimal solution search, initial value setting plays an important role. Onomatopoeia can solve the initial value setting of the interactive design because onomatopoeia can easily describe the visual effect. We proposed a method that allows the users to input their own arbitrary onomatopoeia to design and obtain their desired visual effect. The method hires an onomatopoeia thesaurus map that can convert the arbitrary onomatopoeia to the initial values. We adopted Japanese brush font design system as an experimental example of the interactive design tool in order to evaluate our proposed method. Human subjects used the system and set the initial values of design parameters to design bold and scratched look of target fonts. As a result, the method was able to set more appropriate initial values closer to the optimal values to design the target in comparison with other conventional methods. The proposed method can be useful to solve the initial value setting of the interactive design.

Onomatopoeias are useful expression to communicate intuitive image of some designs. There are some approaches adopt onomatopoeias to design computer graphics, robot motion or Japanese calligraphic fonts. Murata et al. [11] introduced onomatopoeias to their system to design Japanese calligraphic fonts, however, it wasn't easy task to achieve suitable design using only onomatopoeias. On the other hand, Nakamura et al. [12] suggested that achieving an optimal design on an interactive design system corresponds to solving a search problem. To solve the search problem, selection of initial values plays an important role. Nakamura et al. proposed introducing onomatopoeias to set proper initial values of Murata's design system. Nakamura's experimental result showed that the proposed method is useful to achieve an suitable design of the calligraphic font. But the experiment was conducted for the fonts whose appearances were easily described by using onomatopoeias. Our study aims to reevaluate Nakamura's method. We adopted the fonts whose appearances were difficult to describe by onomatopoeias, and conducted an experiment to evaluate the cost to achieve the suitable design. As the experimental result, onomatopoeias can be a useful tool to achieve an suitable design without considering easiness or difficulty to describe by onomatopoeias.

In recent years, devices, such as household electrical appliances, have been created that enable a person using them to control them by pointing their fingers. This is also the case for sharing information with robots, but when a person points his or her finger, it is unknown whether the point position is accurate. In this paper, we investigate the difference between a target point and the actual point that someone has indicated when pointing his or her finger and propose a method for correcting the amount of difference by using the inclusion-exclusion integral.

Recently, educational-support robot have been attracting increasing attention as learning-support devices. Previous research has proposed the sympathy expression method that robot expresses emotions which sympathize with learner. In previous research, the sympathy expression method has been used for the robot which expresses its emotion by face. Sympathy expression method can be contracted based on fuzzy logic. However, no research for sympathy expression method using the robot which expresses its emotion by using body or unifying body and face. Therefore, this paper examines the learning effect and the impression about three types robots which have different emotion expression methods in long term experiment.

With the growth of robot technology, more robots are being designed to support learning. Most studies in the field have focused on robot behavior, with only a few studies focusing on robot utterances. Correspondingly, the manner in which such utterances affect learning is poorly understood. This study investigates the effects of collaborative learning wherein a robot encourages a learner. We conducted an experiment to compare the learning effect in three groups. In the first group, learning was conducted with a robot that supplied praises using onomatopoeia, i.e., the representation of an object or state based on the sounds associated with it. In the second group, a robot supplied praises using adjectives or adverbs. In the third group, a robot supplied praise without using onomatopoeia, adjectives, or adverbs. The results of this study suggest that collaborative learning using the first or third method is more effective than that involving adjective/adverb communication.

Adobe Illustrator provides many tools for a user to create art works. The tools also provide many parameters or options that the user can tune up or select. This study focuses on black-and-white drawings (“Sumi-e” or “Suiboku-ga” in Japanese) on Adobe Illustrator. In order to create black-and-white drawings, the user usually has to select a proper brush from preset brushes Adobe Illustrator provides. But the selection is difficult for the user, especially beginners. This paper proposes a supporting plug-in tool for the user to decide and apply the proper brush. The plug-in can accept an onomatopoeic utterance input which expresses the user's own imagination or impression of the brush. Onomatopoeia is well known as a useful figurative expression for Japanese people to describe their own imagination or impression for something. This paper reports the configuration of the plug-in and illustrated some examples of black-and-white drawings created by using the plug-in. The architecture of the plug-in consists of CNN(convolution neural network), database of brush samples, brush image interpolation, fuzzy-based image expansion and contraction, and so on. CNN allows input of arbitrary onomatopoeias. The brush image interpolation generates a new brush from brush samples in the database. The fuzzy-based image expansion and contraction transforms the new brush into a natural-looked brush form with less jaggy.

Onomatopoeias are useful tool to communicate intuitively and conveniently among people. This paper focused on the utility of onomatopoeias and proposed a design supporting system of Japanese Kanji-calligraphic “Shodo” font using onomatopoeia utterance. The system can generate a variety of scratched- and blurred-look design of “Shodo”. In order to realize the design, the system utilized the correspondence between onomatopoeias and the scratched- and blurred-look. This paper reported the concept and the architecture of the proposed system. And we demonstrated some output examples generated by the system.

In recent years, expression education that improves imagination and communication skills has been of increasing interest. Suitable support and evaluation for each student in expression education is necessary because students have different skill levels. However, many existing educational institutions educate learners in a one-to-many environment. This makes it difficult for teachers to support and evaluate all learners adequately. In this study, we use a robot as an alternative to a teacher in a one-to-one environment. Suzuki et al. proposed a robot for supporting expression education that gives hints to arouse imagination. They suggested that the robot has a positive effect on learning support but cautioned that it is necessary for learners to have an interest in the robot and to pay attention to it in order for the effect to be felt. In this paper, we focus on the nod in non-verbal communication and propose a robot for supporting expression education that nods and gives hints. To determine the effectiveness of the proposed robot, we performed experiments in which we compare its performance with that of a robot that nods but does not give hints, a robot that gives hints but does not nod, and a robot that neither nods nor gives hints.

A proposed robot provides functionality inspired by the behavior of hearing dogs, which assist hearing-impaired people by alerting them to important sounds such as fire and smoke alarms. Hearing dogs use body contact for communication with hearing-impaired people, a process heavily depending on haptic communication. The proposed robot also uses physical contact for urgent notification, like a hearing dog's touching behavior. The robot can move using its wheels and then approach and bump people. To be explicit our proposal, our bumping behavior model was simplified and an experiment was conducted with hearing-impaired participants and a prototype robot. This paper reports the experimental results and discusses effectiveness of the robot's bumping behavior.

Research on physical human-robot interaction has been attracting attention recently, focusing on robot embodiment. The work reported here proposes Active Touch Communication Robot (AcToR), a robot that is modeled on the hearing dog. A hearing dog is a type of dog assist people who are deaf or hard of hearing by alerting their handler to important sounds. AcToR uses the sense of touch to notify a human of the intention to transfer information. For example, when AcToR detects that a cell phone that is in another location has received a call, AcToR moves to the user's location and makes contact with the user's body to notify the user of the incoming call. The AcToR robot is based on the Roomba®and uses the Roomba's bumper and contact sensors to detect contact. This paper reports the results of psychological experiments using the AcToR robot that indicate the feasibility of using touch to transfer information from a robot to a person.

As robot technology grows, more attention is being paid to educational-support robots that assist in learning. For example, an educational-support robot may support students in their school life or help them learn other languages. However, users tend to lose interest in educational-support robots. To solve this problem, a model of emotional expressions has been proposed in human-agent interaction studies. This model, in which the screen agent is programmed to express emotions autonomously, has proven to be effective for interactions between agents and humans. Thus, this paper proposes an emotional expression model for use with educational-support robots. Using simulations, this paper also investigates the number of emotions that a robot can express.

Robots are being used more and more in dangerous environments such as space and disaster areas. However, when robots are at risk in dangerous environments, the time during which robot operators can issue risk avoidance instructions is limited. Therefore, robots should be able to acquire behaviors that enable them to autonomously avoid danger. In this paper, we present a probability-based reinforcement learning (PrRL) method and apply it to robot behavior acquisition.

Recently, more educational-support robots, which support learning, are paid attention to. However, the problem of these robots is that a user loses his/her interest in them. To solve this problem, some studies which focus on emotional expression models have been reported in Human-Agent-Interaction. The model of emotional expressions is defined as the agent expressing its emotions autonomously. Although these models have been shown to be beneficial for effective interaction between an agent and a human, no reports have addressed the educational-support robots using these models. Thus, this paper studies how much learning effect with a robot which expresses the emotion by using the model of emotional expression can be prompted for learners in a collaborative learning.

Embodied expertise, which expresses skills of experts, is a kind of tacit knowledge that is difficult to transfer to another person by means of writing it down or verbalizing it. The aim of our study is to translate embodied expertise into explicit knowledge, i.e. onomatopoeias. We call the onomatopoeias "embodied expertise onomatopoeias" which could facilitate people to intuitively and easily understand the skills. Acquiring "embodied expertise onomatopoeias" is considered as a problem of pattern recognition. Our study adopted a skill of Japanese penmanship "Pen Shodo" which is Japanese calligraphy using pen to be translated to onomatopoeias, and investigated a possibility to construct a classification system for the skill.

We investigated the effect of robot's utterances using onomatopoeia on collaborative learning. The robot was designed to praise or comfort by using onomatopoeia when learners are given problem to solve through a learning system. When learners can correctly solve a problem, the robot praises the learner's success. When learners cannot solve it, the robot comforts the learners to keep working at it. Eight college students learns mathematics by using a learning system with a robot for three weeks and took exams. We found that a robot could comfort learners that used onomatopoeia more than a robot that did not use onomatopoeia. This suggests that the robot that praises or comforts by using onomatopoeia helps learners maintain their motivation in collaborative learning.

We propose a crossover operation for multi-terminal decision diagrams (MTBDDs). To survey this crossover operation, we conducted experiments of the evolution of MTBDDs with and without the proposed crossover operation. We confirmed that MTBDDs that have better fitness were obtained in the evolution of MTBDDs with the proposed crossover operation than without it. We also confirmed that MTBDDs that possess the smaller number of vertices were obtained in the evolution of MTBDDs with the proposed crossover operation than without it.

In this study, we sought to examine how the behavior of a robot can prompt collaborative learning with a human. We focus on constructive interaction that has been regarded as a foundation of collaborative learning and occurs when two learners alternately solve a question. For this, the robot is designed to alternately perform speaker and listener motions for constructive interaction with a human. With the speaker motion, the robot explains a solving method to the partner and solves a question. Moreover, the robot improves its accuracy rate as learning progresses. With the listener motion, the robot does not solve a question and instead pays attention to the partner who is solving the question. The robot learns while solving a question issued by a learning system with a human student. College students recruited as volunteers learned with learning system with the robot for one month and were videoed during that time to see how they learned. The results of examination suggest that the robot prompts learners to learn by constructive interaction in collaborative learning and possibly gains the same learning effect as collaborative learning between two humans.

This study sought to examine how behavior of a robot can prompt learning by observing in collaborative learning. We designed the robot to learn in the same way as a human learner at the when learning begin. Moreover, the robot changes the way of learning to a more effective one as the learning progresses. Ten college students with low level English learned using an English vocabulary learning system with robot for two months and were videoed during that time to see how they learned. We found that learners came to imitate the learning method of the robot and change their way of learning to the more effective one. This suggests that the robot that changes the learning method as the learning progresses prompts learners to learn by observing in collaborative learning.

In line with the effects of Japan's aging society with fewer children, the number of elderly with dementia who live alone and without care is increasing, therefore at-home care is beginning to be required. Doll therapy is a way of at-home dementia care. Doll therapy can help improve mental status of the elderly, because their logical and rational thinking is activated by imaging the child care experience with feeling and emotion at the times. In this research, we consider using Babyloid, a baby type robot, for doll therapy. As a first step of this research, we investigated whether elderly people in a nursing home for the elderly home accept Babyloid.

Given the remarkable recent progress in robotics research, we can envision the day when robots and humans coexist and robots become closely integrated into our daily lives. This means endowing robots with the ability to communicate so they perceive human emotion, adapt their behavior to humans, and sense situations even without explicit instructions. Meanwhile, affective computing, that interprets emotion or other affective phenomena from human biosignals, has emerged as an area of great interest. In addition to biosignals-brain waves, heart rate, pulse, electrical activity, and the like-affective computing is concerned with facial expressions, gestures, and a wide range of other indicators of emotion. Here we explore the latest insights of affective computing in relation to human-robot interaction (HRI). There is good reason to believe robots will soon have the ability to read human emotions, so here we investigate the feasibility of inferring human psychological states from biosensor signals. Obviously, non-invasive biosensors that don't interfere with normal everyday activities would be preferable. A number of inexpensive user-friendly brain-wave sensors have been brought to market recently, and we employ one of these devices, the NeuroSky Mindset EEG neuroheadset, in assessment trials to explore the feasibility of inferring subjective assessments. Using our experimental setup, we find that it is indeed possible to infer subjective assessments from biosignals, and this capability could prove immensely useful for future HRI applications.

A model is described that estimates object posture by using a number of ellipses. First, the ellipses are rotated and arrayed in the annular direction. Next, the ellipses that change long-and-short axis ratio are arrayed in the radial direction. Finally, a radial-ring network model is built by connecting each of these ellipses to form a concentric network. A variable template network (VTN) model is based on the same concept: an ellipsoid is rotated, enlarged and reduced, and then several a 2D appearance figure is projected onto each 2D plane surface. The VTN model is constructed by connecting these figures to form a grid. Although object postures can be estimated using this model, there are multiple figures with the same rotation angle and form in the VTN model. Similarly, since the discrete degree at the time of projection is constant, if the skewness of a 3D object is high, this can not estimate its posture accurately. The proposed model does not suffer these problems, enabling it to estimate object position and posture quickly and accurately.

Given a remarkable recent progress in robotics research, we can envision the day when robots and humans coexist and robots become closely integrated into our daily lives. This means endowing robots with the ability to communicate so they perceive human emotion, adapt their behavior to humans, and sense situations even without explicit instructions. Meanwhile, affective computing, that interprets emotion or other affective phenomena from human biosignals, has emerged as an area of great interest. In addition to biosignals-brain waves, heart rate, pulse, electrical activity, and the like-affective computing is concerned with facial expressions, gestures, and a wide range of other indicators of emotion. Here we explore the latest insights of affective computing in relation to human-robot interaction (HRI). There is good reason to believe robots will soon have the ability to read human emotions, so here we investigate the feasibility of inferring human psychological states from biosensor signals. Obviously, non-invasive biosensors that don't interfere with normal everyday activities would be preferable. A number of inexpensive user-friendly brain-wave sensors have been brought to market recently, and we employ one of these devices, the Neuro Sky Mindset EEG neuroheadset, in assessment trials to explore the feasibility of inferring subjective assessments. Using our experimental setup, we find that it is indeed possible to infer subjective assessments from biosignals, and this capability could prove immensely useful for future HRI applications.

In this paper, we propose a method, using multivalued decision diagrams (MDDs), to obtain motion representation of humanoid robots. Kanoh et al. have proposed a method, which uses multiterminal binary decision diagrams (MTBDDs), to acquire robot controller. However, nonterminal vertices of MTBDDs can only treat values of 0 or 1; multiple variables are needed to represent a single joint angle. This increases the number of the non-terminal vertices and MTBDDs that represent that the controller becomes complex. Therefore, we consider using the MDD, in which its nonterminal vertices can take on multiple output values. To obtain humanoid robot motion representation, we propose evolutionary MDDs and show experimental results comparing evolutionary MDDs and evolutionary MTBDDs through simulations of acquisition of robot motion in this paper. Moreover, we verify whether the evolution of MDD using a memetic algorithm is effective.

It could be very important and useful to enable human stress to be estimated on basis of quantitative values via objective analysis. There have been several approaches to estimate the stress so far. Most of them need a lot of time to measure or calculate it, and what is more, they might cause physical or mental burden for human subjects. The purpose of our study is to propose and develop a method to estimate subjective stress through a simple approach which doesn't cause much burden for users. In this paper we propose a method, which is called TPAf (Turning Point Amplitude via finger plethysmogram). Original TPA is a kind of heart rate variability analysis and needs to attach sensors on his/her breast. Our experimental results indicated that it has a certain relationship between subjective stress and TPAf. A method based on TPAf could be efficient to estimate subjective stress.

The urge theory, in which emotion constitutes an autonomous system, is used to model a self-sufficiency system for communication robots. Using this model, a robot can convey its psychological and physiological conditions to the user through facial expressions, voice, behavior, and other means thereby producing psychological interaction with the user.

A method in which multi-valued decision diagrams (MDDs) are used to acquire robot action rules is proposed. Kanoh et al. have proposed a method, which uses multi-terminal binary decision diagrams (MTBDDs), to acquire robot action rules. But the variables of MTBDDs can only take values of 0 or 1; multiple variables are needed to represent a single joint angle. This increases the number of variables and the MTBDDs that represent the action rules become complex. Here, a method that uses MDDs, in which a single variable can take on multiple values, is proposed and experimental results are shown comparing MTBDDs and MDDs through simulations to acquire robot action rules.

Onomatopoeia refers to words that represent the sound, appearance, or voice of things, which makes it possible to create expressions that bring a scene to life in a subtle fashion. In this study, we propose an onomatopoeia thesaurus map to enable the construction of a map that visually confirms "similarity relationships between a number of onomatopoetic words" and "similarity relationships between unknown onomatopoetic words and existing onomatopoetic words", which are difficult to grasp from a conventional thesaurus. It is also possible to label objects with onomatopoetic words and visualize the similarity relationships between the objects on a map. In this paper, we introduce an example of labeling onomatopoetic words relating to the textures of sweets (desserts).

This paper presents a texture transfer that considers continuous structure of texture patches in target and source images. Previous texture transfers focus on local features, such as color distance, standard deviation and directional factors. In addition to those local factors it might be important and useful to use continuous structure of texture patches in the images of some cases. For instance, we aim at design of "Shodo" fonts based on examples. Shodo, the Japanese calligraphy, is a form of artistic writing used for writing the Japanese and Chinese language. This study proposes and develops a method of designing Japanese calligraphic fonts which contain artistic representations similar to actual Shodo art. Figure 1c illustrates our result which preserves the flow of salient brushwork of the source image (Figure 1a). On the other hand, Figure 1d, which is generated by the algorithm of Texture by numbers [Hertzmann et al. 2001], doesn't seem to express the flow of brushwork of the source. Our approach deals with the flow of brushwork as the continuous structure of texture patches. Our results could be preserve the continuous structure of source texture patches and express the artistic effect of brushwork.

Non-photorealistic rendering (NPR) is a technique used in the field of computer graphics. Here, we restrict our attention to the subset of NPR developed to synthesize drawing and painting techniques. Specifically, we focus on the style of Cubist art, such as the works of Picasso. First, we examine the actual works of Picasso in order to understand and improve the qualities of Cubist style rendering. As a result, the painting composition rules are expected to be extracted from the analysis of Picasso's works. In the present paper, we propose a Cubist style rendering based on the analysis of painting compositions. The present paper describes the proposed method by which to construct a painting composition database and illustrates the rules extracted from the database. We use rough set theory to extract the rules, and using these rules; we render and illustrate a sample image in Cubist style.

A central pattern generator (CPG) and passive dynamic walking (PDW) have attracted much attention in the research field of bipedal locomotion. We describe a motion control method based on dynamic joint passivization for biped robot locomotion. CPG-based motion control is effective for walking on uneven terrain. However, it has serious problems with energy loss. In contrast, PDW saves energy because a robot can walk without any active control or energy input on a downhill slope. However, PDW robot can not walk on uneven terrian, but only on a downhill slope. We think that active walking needs to be mixed with PDW for robot walking. Our motion control method is based on a mixture of the CPG and PDW, that is, the dynamic passivization of joint control. Experiments using the motion control method based on dynamic passivization of joint control successfully generated energy efficient walking and enabled superior gaits.

To improve face-to-face interaction with robots, we developed a model for generating interactive facial expressions by using a simple recurrent network (SRN). Conventional models for robot facial expression use predefined expressions, so only a limited number of expressions can be presented. This means that the expression may not match the interaction and that the person may find the expressions monotonous. Both problems can be overcome by generating expressions dynamically. We tested this model by incorporating it into a robot and comparing the expressions generated with those of a conventional model. The results demonstrated that using our model increases the diversity of face-to-face interaction with robots.

A knowledge-based approach to imitation learning of motion generation for humanoid robots and an imitative motion generation system based on motion knowledge learning and reuse are described. The system has three parts: recognizing, learning, and modifying parts. The first part recognizes an instructed motion distinguishing it from the motion knowledge database by the hidden Markov model. When the motion is recognized as being unfamiliar, the second part learns it using dynamical movement primitives and acquires a knowledge of the motion. When a robot recognizes the instructed motion as familiar or judges that its acquired knowledge is applicable to the motion generation, the third part imitates the instructed motion by modifying a learned motion. This paper reports some performance results: the motion imitation of several radio gymnastics motions.

This paper presents a method that enable a domestic robot to show emotions with its facial expressions. The previous methods using built-in facial expressions were able to show only scanty face. To express faces showing various emotion, (e.g. mixed emotions and different strengths of emotions) more facial expressions are needed. We have therefore developed a system that converts emotions into robot's facial expressions automatically. They are created from emotion parameters, which represent its emotions. We show that the system can generate facial expressions reasonably

Many existing methods of reinforcement learning have treated tasks in a discrete low dimensional state space. However, the smooth control of humanoid robots requires a continuous high-dimensional state space. In this paper, to treat the state space, we proposed an adaptive allocation method of basis functions for reinforcement learning. Grid or incremental allocation methods have previously been proposed for allocation of basis functions. However, these methods may result in the curse of dimensionality, and a fall into local minima. On the other hand, our method avoids local minima, which are assessed by the trace of activity of basis functions. That is, if the current state is determined to have fallen into a local minimum, our method eliminates a basis function, which most affects the state. Moreover our method learns with a low number of basis functions because of the elimination process. In order to confirm the effectiveness of our method, by using computer simulation, a humanoid robot learned the motion of standing up from a chair. This motion was enabled with a small number of basis functions.

The "Ifbot" robot communicates with people by considering its own "emotions" and theirs. Ifbot has many facial expressions to communicate enjoyment. We first attempted to extract characteristics of Ifbot's facial expressions by mapping these to its emotional space, which we discuss in this paper. We applied a five-layer perceptron to the extraction. We also propose a method of seamlessly changing facial expressions using the emotional space. We report on some results of facial changes obtained with the proposed method.