Reflecting on memories has a positive effect on mental health. For robots that interact with older adults, interactions that look back on memories are also an important application area. Despite the importance of such reminiscence, no study has simultaneously addressed methods to both facilitate robots conversing about users’ memories and generate content from those memories. In this article, we propose TRAVOT, a system that explores the events behind travel photos through conversation and generates travel memoirs with such photos. TRAVOT uses a large language model (LLM) for flexible information collection. Moreover, it can deepen the conversation topic to obtain a more profound story from the user via a topic control mechanism with Meta-LLM. This not only elicits information that cannot be obtained from the photos based on a prepared list of questions but also allows for deepening the discussion by generating additional questions. In addition, it can eliminate redundant questions that result from naive use of an LLM by applying matching judgment with certain required questions. We conducted a user experiment to evaluate TRAVOT’s effectiveness, and we found that the participants could recall more interesting and unusual things that happened during their trips when they had conversations with TRAVOT. The users could also reminisce about their trips when they read the travel memoirs generated by TRAVOT. In addition, TRAVOT increased the amount of information contained in the conversations and travel memoirs.

This paper proposes RIMER, a dialogue facilitation system specifically designed to support situation-aware remote reminiscence therapy using a shoulder-mounted robot. Reminiscence therapy is a psychological intervention that promotes cognitive improvement by encouraging individuals to recall and talk about their past experiences. Traditional reminiscence therapy typically involves face-to-face conversations guided by pre-selected photographs. In contrast, RIMER dynamically captures a local companion’s current surroundings through a camera mounted on the robot and generates situated questions based on the observed scene and dialogue history. This allows a remote therapy recipient to naturally recall memories associated with what they are presently seeing and recent conversations, enabling more spontaneous and contextually relevant reminiscence. The situated therapy is conducted remotely, without the need for physical co-presence. To evaluate the effectiveness of RIMER, we conducted a comparative study with three groups: one using RIMER with reminiscence support, one with facilitation but without reminiscence, and one with no facilitation. We measured dialogue volume during the sessions and analyzed the effects of each condition on facilitation and memory recall. Results showed that the group using RIMER exhibited more conversation and stronger memory recall than the other groups.

Therapeutic robots are used in facilities for older people, but many of them can only provide mechanical responses to users. To improve the quality of interactions, it is crucial to address emotional consistency. Thus, we propose Semantic Babbling, a system that enables interaction with users by utilizing the baby robot's “inner voice”. By generating inner voices and selecting babbling based on sentiment, Semantic Babbling aims to mimic infant-like responses. A crowd-sourcing survey demonstrated the significance of emotional consistency and the display of inner voice in Semantic Babbling, revealing that it improves the impression of baby robots.

To construct a conversational system for robots that considers the interpersonal relationships among three parties, this paper proposes a system called RelBot. The system uses a large language model (LLM) to estimate the current interpersonal relationship and ideal balanced interpersonal relationship from the content of a three-way conversation between a user and two robots; then, it generates statements for the robots to establish the ideal balanced relationship according to the user's desire and Heider's balance theory. The challenging points here are to investigate whether an LLM can recognize the current relationships between a user and each robot and between two robots from the content of their conversation, and whether it can estimate the relationships that the user wants to achieve. Moreover, RelBot provides a new way to adjust the three-way relationship to the ideal balanced one. We conducted two evaluation experiments. The results indicated that participants could intentionally change the relationships to the desired ones, while RelBot accurately recognized both the current relationship and the participant's desired relationship. Furthermore, the final relationship in the free conversation with two robots could be balanced through the effectiveness of RelBot's generated statements.

In the field of robotics, tag systems play an important role in various applications, such as object identification and robot control in real-world environments. While typical visual markers use two-dimensional (2D) patterns and RGB cameras for recognizing object IDs and poses, achieving long-distance recognition necessitates increasing marker size and camera magnification to ensure the required resolution. Furthermore, the growing adoption of event cameras in robotics captures rapid changes in pixel brightness but faces limitations in recognizing stationary 2D markers. Although compact blinker markers using blinking light-emitting diodes (LEDs) achieve long-distance recognition, they are constrained by the number of IDs or recognition speed when used with standard RGB cameras. In addition, recognizing object pose using only a single blinking LED presents challenges. To address these challenges, we introduce ‘Bicode,’ an indoor visual marker designed for event cameras. Bicode seamlessly integrates 2D and blinker markers within a single marker unit. We have developed prototypes of 2.5, 5, and 10 cm square acrylic 2D markers, each equipped with a single LED blinking at 1 kHz, enabling recognition with an event camera. Our experiments revealed the effects of marker size, LED light quantity, recognition distance, and angle, external lighting conditions, and camera or marker movement on accuracy. Notably, using the 5 cm marker, we confirmed its compatibility to recognize IDs at distances exceeding 20 m, and pose recognition at 2.5 m was confirmed.

When individuals are talking while performing multiple tasks at the same time, it is sometimes easy to miss parts of a conversation and misinterpret subsequent statements or have difficulty following the conversation. In this work, we aim to identify statements that may lead to misinterpretation of the subsequent statement if missed and to prevent communication discrepancies. Although there have been several attempts to present images and text that provide topics to support conversation, there is currently no system that supports conversation by taking interpretability into account. We propose a conversation support system SCAINs Presenter that presents Statements Crucial for Awareness of Interpretive Nonsense (SCAINs), which are statements that are important for interpreting other sentences and are extracted by reproducing the interpretations of those who missed part of the conversation and those who did not. The unique point of the SCAINs Presenter is to display extracted sentences that influence the context of the subsequent dialogue by taking into account their interpretability. In particular, since SCAINs are sentences that may cause misinterpretation of the subsequent dialogue if they are absent, the SCAINs Presenter helps the users to be aware of the possibility of a conversation gap coming from the misinterpretation. Our experiments show that when SCAINs are omitted, the intention of the following statements often becomes unclear, and the meaning of the following statements changes. We also found that SCAINs can capture a unique aspect different from the merely important statements. Moreover, the results of case studies in a realistic setting suggest that looking at SCAINs encourages conversation participants to switch their focus from a subtask chat to an ongoing conversation that is a primary task. Our research clarifies the linguistic processing underlying the identification of high-context utterances and demonstrates the effectiveness of using them to support real person-to-person interactions.

During remote conversations, communication breakdowns often occur when a listener misses certain statements.Our objective is to prevent such breakdowns by identifying Statements Crucial for Awareness of Interpretive Nonsense (SCAINs).If a listener misses a SCAIN, s/he may interpret subsequent statements differently from the speaker's intended meaning.To identify SCAINs, we adopt a unique approach where we create a dialogue by omitting two consecutive statements from the original dialogue and then generate text to make the following statement more specific.The novelty of the proposed method lies in simulating missing information by processing text with omissions.We validate the effectiveness of SCAINs through evaluation using a dialogue dataset.Furthermore, we demonstrate that SCAINs cannot be identified as merely important statements, highlighting the uniqueness of our proposed method.

Non-verbal communication is an essential part of both human-human and human-robot interaction. The chameleon effect, unlike facial expressions or gestures, is a special one in non-verbal communication because of its nature that both the person conveying this effect and other interaction partners should not be conscious of it. With remote meetings becoming common in people’s work and daily life, it has been generally aware that the need of improving the experience of remote meetings is increasing. This paper proposed a system, named Telepresence Chameleon, which implements the chameleon effect on the telepresence robots. Telepresence Chameleon applies the chameleon effect to the telepresence robots’ video stream as real-time feedback according to users’ movements and restrains it in a subtle range such that users are kept unconscious of it. The result of the experiments shows that Telepresence Chameleon provided a better user experience than a normal telepresence robot.

When people are talking together in front of digital signage, advertisements that are aware of the context of the dialogue will work the most effectively. However, it has been challenging for computer systems to retrieve the appropriate advertisement from among the many options presented in large databases. Our proposed system, the Conversational Context-sensitive Advertisement generator (CoCoA), is the first attempt to apply masked word prediction to web information retrieval that takes into account the dialogue context. The novelty of CoCoA is that advertisers simply need to prepare a few abstract phrases, called Core-Queries, and then CoCoA automatically generates a context-sensitive expression as a complete search query by utilizing a masked word prediction technique that adds a word related to the dialogue context to one of the prepared Core-Queries. This automatic generation frees the advertisers from having to come up with context-sensitive phrases to attract users’ attention. Another unique point is that the modified Core-Query offers users speaking in front of the CoCoA system a list of context-sensitive advertisements. CoCoA was evaluated by crowd workers regarding the context-sensitivity of the generated search queries against the dialogue text of multiple domains prepared in advance. The results indicated that CoCoA could present more contextual and practical advertisements than other web-retrieval systems. Moreover, CoCoA acquired a higher evaluation in a particular conversation that included many travel topics to which the Core-Queries were designated, implying that it succeeded in adapting the Core-Queries for the specific ongoing context better than the compared method without any effort on the part of the advertisers. In addition, case studies with users and advertisers revealed that the context-sensitive advertisements generated by CoCoA also had an effect on the content of the ongoing dialogue. Specifically, since pairs unfamiliar with each other more frequently referred to the advertisement CoCoA displayed, the advertisements had an effect on the topics about which the pairs spoke. Moreover, participants of an advertiser role recognized that some of the search queries generated by CoCoA fit the context of a conversation and that CoCoA improved the effect of the advertisement. In particular, they learned how to design of designing a good Core-Query at ease by observing the users’ response to the advertisements retrieved with the generated search queries.

When a user manipulates a system, a user input through an interface, or an operation, is converted to the user’s intended action according to the mapping that links operations and actions, which we call “operation mapping”. Although many operation mappings are created by designers assuming how a typical user would operate the system, the optimal operation mapping may vary from user to user. The designer cannot prepare in advance all possible operation mappings. One approach to solve this problem involves autonomous learning of an operation mapping during the operation. However, existing methods require manual preparation of scenes for learning mappings. We propose advantage mapping, which enables the efficient learning of operation mappings. Working from the idea that scenes in which the user’s desired action is predictable are useful for learning operation mappings, advantage mapping extracts scenes according to the magnitude of entropy in the output of the action value function acquired from reinforcement learning. In our experiment, the user’s ideal operation mapping was more accurately obtained from the scenes selected by advantage mapping than from learning through actual play.

This paper proposes VISTURE, a system for generating a robot’s gesture and speech by using video as input. VISTURE assumes a situation in which a robot conveys what it saw with a camera to a person who was absent. The value of this paper is that we have performed a case study to investigate the expressions that Japanese people use to describe video scenes, and used the results to build VISTURE. In particular, we found classification of expressions depicting the video scenes throughout the case study: Foreground information that is the relevant event of the scene and Background one that is not the main point of the description giving the entire scene. Foreground and Background are referred in combination. VISTURE employs the classification to generate human-like expressions. Moreover, we designed the method to determine Foreground and Background, and it can generate multiple combinations of expressions. We investigated the people’s impression of a robot performing the gestures and speech generated by VISTURE to evaluate the quality of those gestures and speech. The results showed that the robot was perceived as more likable and capable when it performed gestures.

The main goal of this workshop is to establish awareness of the emerging field of socially assistive shopping robots in human-robot interaction (HRI) and, simultaneously, to foster interdisciplinary approaches that combine the development of social robots with a sequential and embodied perspective regarding mutual processes of understanding in shopping interactions with assistive robots.

In this work, we present a system that sequentially generates advertisements within the context of a dialogue. Advertisements tailored to the user have long been displayed on the digital signage in stores, on web pages, and on smartphone applications. Advertisements will work more effectively if they are aware of the context of the dialogue between the users. Creating an advertising sentence as a query and searching the web by using that query is one way to present a variety of advertisements, but there is currently no method to create an appropriate search query for the search in accordance with the dialogue context. Therefore, we developed a method called the Conversational Context-sensitive Advertisement generator (CoCoA). The novelty of CoCoA is that advertisers simply need to prepare a few abstract phrases, called Core-Queries, and then CoCoA dynamically transforms the Core-Queries into complete search queries in accordance with the dialogue context. Here, “transforms” means to add words related to the context in the dialogue to the prepared Core-Queries. The transformation is enabled by a masked word prediction technique that predicts a word that is hidden in a sentence. Our attempt is the first to apply masked word prediction to a web information retrieval framework that takes into account the dialogue context. We asked users to evaluate the search query presented by CoCoA against the dialogue text of multiple domains prepared in advance and found that CoCoA could present more contextual and effective advertisements than Google Suggest or a method without the query transformation. In addition, we found that CoCoA generated high-quality advertisements that advertisers had not expected when they created the Core-Queries.

In this paper, we introduce d3rlpy, an open-sourced offline deep reinforcement learning (RL) library for Python. d3rlpy supports a set of offline deep RL algorithms as well as off-policy online algorithms via a fully documented plug-and-play API. To address a reproducibility issue, we conduct a large-scale benchmark with D4RL and Atari 2600 dataset to ensure implementation quality and provide experimental scripts and full tables of results. The d3rlpy source code can be found on GitHub: https://github.com/takuseno/d3rlpy.

User interfaces have been designed to fit typical users and their usage styles as assumed by designers. However, it is impossible to cover all the possible use cases. To address this problem, we propose$Q$-Mapping, which is a method for user interfaces to acquire the operation mapping, or mapping from user operations to their effects.$Q$-Mapping has an advantage over previous techniques in that it can acquire operation mapping interactively. The core idea of$Q$-Mapping is that what a user selects as an ideal action has a tendency to be the same as the action that has the highest$Q$-value. On the basis of this concept, we defined the operation-action value function, which can be calculated from the value that a user expects to gain when a particular mapping is given in that state and is updated each time an operation occurs. We conducted a simulation experiment and a user study to investigate the$Q$-Mapping performance and the effects of the acquisition of interactive operation mapping. The simulation results showed that the changeability of operation mapping could be controlled by a coefficient called the balancing parameter. As for the user study, we found that$Q$-Mapping with a balancing parameter that decays with time was able to acquire operation mapping that was easy for users to understand. These results demonstrate the importance of balancing consistency and adaptability in the interactive acquisition of operation mapping.

To create dialogue systems that provide information a user needs to know at an opportune moment, it is important to infer the user’s mental states such as his/her beliefs and desires. There are two types of study on inferring beliefs and desires: one type infers them from actions and the other infers them from the content of utterances. However, a method to infer beliefs and desires from both kinds of inference in an integrated way has not yet been established. In this paper, we propose Multimodal Inference of Mind Simultaneous Contextualization and Interpreting (MIoM SCAIN), a system for sequentially inferring users’ beliefs and desires on the basis of their walking behaviors and the content of their utterances. In our evaluation, we compared inferences of MIoM SCAIN with those of baselines that use either walking behaviors or the content of utterances. MIoM SCAIN’s predictions showed more correlation with subjective judgements compared with the baselines, indicating that the inference of beliefs and desires from both walking behaviors and utterance content is possible.

We studied the programming and debugging processes of an autonomous mobile social robot with a focus on the programmers. This process is time-consuming in a populated environment where a mobile social robot is designed to interact with real pedestrians. From our observations, we identified two types of time-wasting behaviors among programmers: cherry-picking and a shortage of coverage in their testing. We developed a new tool, a test generator framework, to help avoid these testing time-wasters. This framework generates new testing scenarios to be used in a simulator by blending a user-prepared test with pre-stored pedestrian patterns. Finally, we conducted a user study to verify the effects of our test generator. The results showed that our test generator significantly reduced the programming and debugging time needed for autonomous mobile social robots.

Building an interactive artificial intelligence that can ask questions about the real world is one of the biggest challenges for vision and language problems. In particular, goal-oriented visual dialogue, where the aim of the agent is to seek information by asking questions during a turn-taking dialogue, has been gaining scholarly attention recently. While several existing models based on the GuessWhat?! dataset [10] have been proposed, the Questioner typically asks simple category-based questions or absolute spatial questions. This might be problematic for complex scenes where the objects share attributes, or in cases where descriptive questions are required to distinguish objects. In this paper, we propose a novel Questioner architecture, called Unified Questioner Transformer (UniQer), for descriptive question generation with referring expressions. In addition, we build a goal-oriented visual dialogue task called CLEVR Ask. It synthesizes complex scenes that require the Questioner to generate descriptive questions. We train our model with two variants of CLEVR Ask datasets. The results of the quantitative and qualitative evaluations show that UniQer outperforms the baseline.

With digital signage and communication robots, digital agents have gradually become popular and will become more popular. It is important to make humans notice the intentions of agents throughout the interaction between them. This paper is focused on the gaze behavior of an agent and the phenomenon that if the gaze behavior of an agent is different from human expectations, human will have a incongruity and feel the existence of the agent's intention behind the behavioral changes instinctively. We propose PredGaze, a model of estimating this incongruity which humans have according to the shift in gaze behavior from the human's expectations. In particular, PredGaze uses the variance in the agent behavior model to express how well humans sense the behavioral tendency of the agent. We expect that this variance will improve the estimation of the incongruity. PredGaze uses three variables to estimate the internal state of how much a human senses the agent's intention: error, confidence, and incongruity. To evaluate the effectiveness of PredGaze with these three variables, we conducted an experiment to investigate the effects of the timing of gaze behavior change and incongruity. The experimental results indicated that there were significant differences in the subjective scores of the naturalness of agents and incongruity with agents according to the difference in the timing of the agent's change in its gaze behavior.

Automatic driving systems not only for cars, but also for wheelchairs are being developed. Improving the operability and safety of electric wheelchairs is an important issue. For example, if a driving system changes the speed of the vehicle without a driver's operation, it makes the driver uneasy. We developed a system to address this uneasiness. Our system, called the MIZUSAKI system, notifies drivers of a change in the speed gain, which is controlled by the system, before the change. This system uses 4 in-screen effects, which are intended to be seen in the peripheral vision and do not inhibit drivers' attention to driving. In developing this system, we have always considered the notification timing to be a key factor. We tested this system to find the best notification timing and found that if the anticipatory timing is 3 seconds before the speed gain changes or later.

Most of agents that learn policy for tasks with reinforcement learning (RL) lack the ability to communicate with people, which makes human-agent collaboration challenging. We believe that, in order for RL agents to comprehend utterances from human colleagues, RL agents must infer the mental states that people attribute to them because people sometimes infer an interlocutor's mental states and communicate on the basis of this mental inference. This paper proposes PublicSelf model, which is a model of a person who infers how the person's own behavior appears to their colleagues. We implemented the PublicSelf model for an RL agent in a simulated environment and examined the inference of the model by comparing it with people's judgment. The results showed that the agent's intention that people attributed to the agent's movement was correctly inferred by the model in scenes where people could find certain intentionality from the agent's behavior.

In interactions, estimating how much information is being shared between participants is one of the crucial aspects that make the interaction more lively and enhance each participant's sense of understanding of the others. In this paper, we propose a model to estimate how much information is being shared between participants in a conversation. In the proposed model, we considered not only the content of the utterance but also the timing of the utterance. To verify the validity of our model, we implemented a simulator of a party game called Word Wolf, which requires information sharing estimation as the part of the game, and simulated the participants' behavior. Through the simulation, we showed that utterance timing is an important backchannel when estimating information sharing.

In research on semi-autonomous telepresence robots, a problem in which remote operators become frustrated with autonomous operations that do not match their intention has been reported. However, in previous research, a general-purpose method for automatically switching between remote and autonomous operations has not been proposed. In this paper, through the use of a general purpose arbitration model, called the accumulator based arbitration model (ABAM), we propose an adaptive switching architecture for remote and autonomous operations, named "One Minder." We incorporated One Minder into a semi-autonomous telepresence system autonomizing contingent behaviors, and conducted experiments to verify its utility using a robot implementing the proposed architecture. As the experiment results indicate, it was shown that One Minder can adaptively switch between remote and autonomous operations without manual switching. In addition, One Minder was also shown to reduce the operational load and frustration given to a remote operator by allowing the arbitration to properly output an autonomous operation.

Shared autonomy is a situation in which agents adapt to users based on feedbacks to jointly accomplish tasks. In the case of semiautonomous agents such as mobile robots operational load can be reduced by adaptively automating their behavior. Machine learning is one of the method for adapting teleoperated agents to users as control tendency depends on users and environments. It is difficult to regard user inputs as supervisions because user controls are not always available during descisionmaking processes for agents that continually make decisions (such as navigation robots).

Several benefits obtained using multiple robots in conversation have been reported in the human-robot interaction field. This paper first presents pre-trial results by which elderly people assigned a lower rating to a conversation with two robots than to one with a single robot. Observations of the trial suggest the hypothesis that an inappropriate spatial arrangement between robots and humans increases the workload in a conversation. Reducing the workload is important, especially when robots are used by elderly people. Therefore, we specifically examine the workload that is influenced by the spatial arrangement in group conversation. To verify the hypothesis, we use a NASA-TLX and a dual-task method to evaluate the workload and to conduct a comparative experiment in which the participant talks with two robots in two spatial arrangements. We also conduct a case study for elderly people in the same conversational conditions. From these experiments, we demonstrate that the spatial arrangement in which people cannot see both robots simultaneously increases their conversational workload and decreases their evaluation of the dialogue compared to a spatial arrangement by which people can see both robots simultaneously. We also show that the primary cause of the workload by positioning is not physical but mental.

People engage in social coordination without explicitly communicating when they are conflicting over spatial resources, e.g., a shop clerk who yields to customers the best place to view products. In this study, we proposed a method that achieves such social coordination with a robot. Our idea is that the social coordination between two agents can be represented as utility-maximizing behavior for joint utility rather than just by a single agent utility. That is, given that each agent's reasonable behavior can be represented as utility-maximizing behavior for single agent utility, we model each agent's plans for himself as well as for the partner agent. Moreover, superiority relationships exist in this joint-utility computation. Since each agent knows such superiority relationships, social coordination can be modeled as utility-yielding behavior based on informed superiority. We specifically focus on looking-together situations for which we developed a utility model. With simulations, we investigate whether the above joint-utility-based modeling successfully reproduces social coordination in looking-together situations. We conducted an experiment in a situation where a tele-operated robot and a customer together look at products in a shop environment. Our experimental results show that our proposed method enables the robot to socially coordinate spatial resources, yielding significantly more thoughtful, less-self-centered, and appropriate impressions than the alternate robot.

In cooperation, the workers must know how co-workers behave. However, an agent's policy, which is embedded in a statistical machine learning model, is hard to understand, and requires much time and knowledge to comprehend. Therefore, it is difficult for people to predict the behavior of machine learning robots, which makes Human Robot Cooperation challenging. In this paper, we propose Instruction-based Behavior Explanation (IBE), a method to explain an autonomous agent's future behavior. In IBE, an agent can autonomously acquire the expressions to explain its own behavior by reusing the instructions given by a human expert to accelerate the learning of the agent's policy. IBE also enables a developmental agent, whose policy may change during the cooperation, to explain its own behavior with sufficient time granularity.

This study addresses human-robot interactions in a controlled negotiation environment. The aim is to prove that a robot, given its limitations, can win a non-equilibrium based negotiation against a human by convincing him/her. To do so, a behavioral model based on decision trees is proposed, which chooses behavior and action of the robot adaptively depending on the circumstances, robot's intention and human's past response. An experiment under two conditions was conducted:one where the robot was set to play the Desert Survival Situation negotiation game against 10 humans; and one where the robot was compared to other system with the same knowledge about the game but without the behavioral and action generator model. The extracted conclusions were that the robot could win the game in most of the cases, convincing the human. The results also show that its performance is significantly better than the human's and that the other system's robot.

We aim to develop a walking partner robot with the capability to select small-talk topics that are associative to visual scenes. We first collected video sequences from five different locations and prepared a dataset about small-talk topics associated to visual scenes. Then we developed a technique to associate the visual scenes with the small-talk topics. We converted visual scenes into lists of words using an off-the-shelf vision library and formed a topic space with a Latent Dirichlet Allocation (LDA) method in which a list of words is transformed to a topic vector. Finally, the system selects the most similar utterance in the topic vectors. We tested our developed technique with a dataset, which successfully selected 72% appropriate utterances, and conducted a user study outdoors where participants took a walk with a small robot on their shoulder and engaged in small talk. We confirmed that the participants more highly perceived the robot with our developed technique because it selected appropriate utterances than a robot that randomly selected utterances. Further, they also felt that the former type of robot is a better walking partner.

It is important for a robot to appropriately respond to its surrounding environment and events to communicate smoothly with humans as opposed to following fixed movements specified in advance. In this paper, a simple bi-layered architecture (SB architecture) is proposed to integrate behaviors in two stages: prioritization and weighted averaging. In addition, SB architecture integrates voluntary movements by prioritization and integrates involuntary and reflex movements by weighted averaging, thereby generating robot behaviors that immediately respond to events that occur around the robot. What advantages SB architecture has are that it can easily generate various behaviors by combining multiple behaviors and that it is possible to propose a simple design for robot behavior. Furthermore, since behavior occurs in response to sensors, the robot's behaviors are reactive to the surrounding environment and events. In particular, if a robot performs the behavior necessary for communication with humans, such as changing its gaze and gestures, it is possible to ensure that the robot possesses liveness to promote communication with humans. In addition, since SB architecture has a simple structure to facilitate the design of robots, it is possible to automatically set parameters by optimizing the parameters using pairs of sensor information and ideal behaviors. The optimization of the parameters leads to generating behaviors characterized with liveness by appropriately combining behaviors.

In this paper, we focus on “silence,” which appears as a gap or delay in giving a response during a conversation and is one of the most important factors to consider to have a more natural conversation with robots. In the conversation between a human and a robot, silence can be divided into two parts: first, a silence that a human uses for a robot and second, a silence that a robot takes for a human. Therefore, we conducted a conversation test between a human and a robot in order to clarify the following two points: one, whether humans use silence for a robot and two, how silence used by a robot can be interpreted by humans. The results of the experiment indicate that humans certainly use silence for a robot for some reasons. Participants were asked to label the silences in four different types: Semantic Silence, Syntactical and Grammatical Silence, Interactive Silence, and Robotic Silence. As a result of this classification, there were cases where humans used Interactive Silence to be concerned for a robot, similar to that in case of a human conversation partner. It is now clear that humans use and regard silence in a form closer to a human conversation partner rather than a machine partner while in conversation with a communication robot. In particular, we found that sometimes humans use silence in social sense such as Interactive Silence, which is for the consciousness of a conversation partner.

This paper proposes a framework for an Agent Auto-Generation System (AAGS) which gets information from sensor devices and improvises a conversational agent from arbitrary things. AAGS has agent-types to prepare a perception for an improvised agent according to the shape of the agentization targets. It also has virtual-input, which generates knowledge representation related to information extracted from the networked sensor devices. The virtual-input employs a viewpoint based on the agent-type to generate a knowledge expression, and gives it to the improvised agent. Experiments to evaluate AAGS revealed that it is necessary to consider the perception of the improvised agents based on the agent-types. In addition, the agent's viewpoint for the perception has an effect on how people recognize the improvised agent.

A migratable agent, which has the function of providing an interface between the user and the devices with which it needs to interact, can provide the user with continuous assistance. The relationship established between a human and the agent would enable the user to operate home appliances smoothly. However, few studies which have addressed the primary questions that arise in this regard; for example, how and when the agent should execute the task requested by the user. This paper proposes the use of a design principle based on the BDI model for a migratable agent to ensure that it is capable of carrying out user's tasks appropriately. The BDI model enables the agent to determine the scope of continuing actions to achieve its intended target. The BDI model allows the agent to execute tasks in the clear range of achieving intentions and guarantees the completion of tasks within a reasonable range. We investigated the validity of adopting the BDI model by obtaining feedback via questionnaires related to the design of the migratable agent. The results of the questionnaires indicated that the BDI model would be able to facilitate the design of the migratable agent.

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.

In this paper, we propose DECoReS (Degree Expressional Command Reproducing System) that allows a powered wheelchair to travel autonomously through commands that include "degree expressions" depending on particular users and environments. When users control a wheelchair through voice commands, they can sometimes give such orders as "go straight speedily" and "curve to the right widely" to qualify the traveling commands. As these examples illustrate, optional words called "degree expression" are appended to the commands. Because degree expressions are ambiguous, traveling styles described with such expressions are altered depending on the users and environments. DECoReS realizes the travels suited per user by learning degree expressional commands and traveling data from the users. DECoReS also reproduces travels suited for a current environment that a user is about to drive by exacting the data with a map similar to the current environment. Our experiments show that DECoReS can reproduce different travels depending on degree expressional commands, users, and environments.

The purpose of our study is to investigate human teaching behavior and robot learning behavior when a human teaches a robot. Agents for learning support need to build a pedagogical relationship, in which a teacher agent and a student agent change their behaviors as they recognize the other's characteristic behaviors. In order to investigate how a robot that behaves as a student should respond to humans' teaching behaviors in a pedagogical relationship between human and robot, we conducted a case study using a game played on a tablet with a robot. In the case study, we analyzed how humans changed their teaching behaviors when the humanoid robot failed to understand what they taught. From the results of this case study, we observed that some subjects carefully taught the robot in each trial in order to allow the robot to understand the subjects. Moreover, we also observed that subjects' teaching behavior changed when the subject received feedback from the robot about the teaching.

We propose an active display robot for tele-communication system combining 3-axis display arm manually controlled by touch input, and automatically controlled by human tracking. This system is designed to present distance change behavior between user and robot by implementing two types of user interaction during communication; 1) Providing robot's movement to follow the user who is far from or going to pass by the display robot, 2) providing touchable display to activate and manipulate the direction of the display during the user is sited in front of the display system. Both local and remote users use the same system to communicate, make user's attention to distanced user via the system. Manipulating the movable part of the display by the movement of the finger of the user's touch makes the display correspond to user's intention and attention. There are two advantages of the movable display by the operation of the user's touch. One is to enhance remote communication by making the display arm moving with touch operation. Second is to enable the transmission of non-verbal information through the operation of the display. We designed hardware and software associated with user behavior and user input via the display. It is expected that the system will solve that the problem of misunderstanding of remote device operation, then it contribute to intuitive operation. In order to evaluate how this system does contribute to intuitive operation, we conducted an assessment experiment by using Questionnaire of Likert scale and interview. We verified that the display can move in response to operator's touch input contribute to intuitive experience.

The study of the telepresence robot as a tool for telecommunication from a remote location is attracting a considerable amount of attention. However, the problem arises that a telepresence robot system does not allow the volume of the user's utterance to be adjusted precisely, because it does not consider varying conditions in the sound environment, such as noise. In addition, when talking with several people in remote location, the user would like to be able to change the speaker volume freely according to the situation. In a previous study, a telepresence robot was proposed that has a function that automatically regulates the volume of the user's utterance. However, the manner in which the user exploits this function in a practical situation needs to be investigated. We propose a telepresence conversation robot system called “TeleCoBot.” TeleCoBot includes an operator's user interface, through which the volume of the user's utterance can be automatically regulated according to the distance between the robot and the conversation partner and the noise level in the robot's environment. We conducted a case study, in which the participants played a game using TeleCoBot's interface. The results of the study reveal the manner in which the participants used TeleCoBot and the additional factors that the system requires.

The Lombard effect is the involuntary tendency of speakers to increase their vocal effort when speaking in a loud noise to enhance the audibility of their voice. There is a problem in telecommunication due to the Lombard effect. A speaker talks at a louder volume than necessary for the conversation partner at a remote location. This paper proposes a volume model that is required in order to automatically adjust the volume of an operator's voice at a remote communication via a telepresence robot, and develops an optimal volume control system LombaBot equipped on a telepresence robot with the model. The volume model measures the level of noise around the robot and the distance between a conversation partner and the robot to adjust the volume of the operator's voice. It has two types of volume adjustments. Those are called comfortable volume and secret talk volume. LombaBot enables people at a remote site to listen comfortably to the voice of a robot operator. Moreover, the operator is able to talk in low voices when s/he wants to talk in secret with nearby people. We confirmed that LombaBot adjusted the volume of an operator's voice properly in the noisy remote location.

Mutual adaptation plays an important role when people build a relation with the others and communicate with them. We can observe the adaptations not only in human-human communication but also in human-animal communication. For example, a service dog learns proper behaviors while reading the verbal and nonverbal expressions of a human. Also, the human behaves in response to what the dog learns. The workshop named "Cognitive Interaction Design" discusses the nature of the mutual adaptation between people or between a person and an animal. Moreover, it discusses the area of a field where the design of using the mutual adaptation for an interactive system shows exceptional performances.

Researchers have developed computer systems which interact with people by recognizing situations around people. On the other hand, people behave socially based on perceived relations with others, being aware of a gaze from others, public pressure from others, and so on. If the computer systems can recognize the social relation and manage/control it, a new interactive service will emerge by enhancing the social skill of people. Designing, visualizing, and simulating relations also prepare a new socially interactive environment for people. The workshop named "Augmented Sociality and Interactive Technology" discuss the technologies which can deal with social relations and augment the social behaviors of people. Moreover, the workshop discuss interactive services using the technologies of the augmented sociality.

People spend their everyday life while expecting social relations between them and the others. However, the expectation causes a trouble or a misunderstanding because they grasp the relations from their subjective viewpoint. It is useful that a computer software assists people to grasp the relations by showing the possible variations of the relation. This paper proposes a simulator named SB Simulator that accepts current relations which a user grasps and simulates the course of the development of relations between them based on Heider`s balance theory and Socion theory. The aim of SB Simulator is to make the user know what changes in the relations happen if s/he takes an action. In particular, SB Simulator generates possible initial relations before starting the simulation, which compensate for the possibility that the user grabs the current relations incorrectly from her/his subjective viewpoint. The function of generating the initial relations has a role in raising a user's awareness of the other possibilities of relations. Moreover, since SB Simulator employs Socion Theory to express the relations, it can distinguish relations which people recognize subjectively from the ones existing objectively, and simulate possible relations based on dynamics between the subjective and objective relations. The paper evaluates SB Simulator in terms of whether the results of the simulation are acceptable in contrast to the typical course of relation development. The result indicated that SB Simulator simulated the relations properly based on the initial relations.

This paper models the concept of the "territory" of shops. First, we interviewed three shopkeepers and found that they perceived the space near their shop as their territory and that they interpreted some types of behaviors as invasive. Second, we confirmed that potential visitors share this notion of territory. We also confirmed that the size of the territory depends on the characteristics of a shop's facade. While there is little territory in front of walls, there is more territory in front of shelves and entrances. Our robot traversed around two real shopping malls that included 50 shops and took 3-D scans of their environment shapes. Each shop's facade was analyzed and the shop territory was computed. The computation results match people's perception. The recognition rate accuracy reached 93.5% for the territory areas. User evaluations in a virtual shop environment confirmed that a robot with a territory model behaves better than one without it.

FlashTouch is a new technology that enables data communication between touchscreen-based mobile devices and digital peripheral devices. Touchscreen can be used as communication media using visible light and capacitive touch. In this paper, we designed a stylus prototype to describe the concept of FlashTouch. With this prototype, users can easily transfer data from one mobile device to another. It eliminates the complexity associated with data sharing among mobile users, which is currently achieved by online data sharing services or wireless connections for data sharing that need a pairing operation to establish connections between devices. Therefore, it can prove to be of particular significance to people who are not adept at current software services and hardware functions. Finally, we demonstrate the valuable applications in online settlements via mobile device, and data communication for mobile robots.

This paper proposes a blended reality agent, called BReA, whose body presents and transfers itself between real and virtual environments. BReA can communicate with people through not only real-world communication but also virtual communication. Recent research studies have shown the merits and demerits of communication with robotic agents under a real-world environment, and on-screen agents in a virtual world. BReA, whose body can be located in both real and virtual worlds, possesses the merits of both robotic and on-screen agents. In addition, the feature through which BReA communicates with people in real and virtual environments allows users to acknowledge real-world objects based on its references from a virtual environment. We conducted a field test in a supermarket to confirm whether customers can engage themselves in communication with BReA. An analysis of the consumer reactions confirmed that customers definitely recognized that the directions of the pointing gestures performed by BReA in a virtual environment were oriented outside of the display. Moreover, we observed some customers approaching closer to BReA as it transferred from a real environment into a virtual world. These results demonstrated that BReA succeeds in immersing customers in its presentation.

We present a sensing technology and input method that uses skin deformation estimated through a thin band-type device attached to the human body, the appearance of which seems socially acceptable in daily life. An input interface usually requires feedback. SenSkin provides tactile feedback that enables users to know which part of the skin they are touching in order to issue commands. The user, having found an acceptable area before beginning the input operation, can continue to input commands without receiving explicit feedback. We developed an experimental device with two armbands to sense three-dimensional pressure applied to the skin. Sensing tangential force on uncovered skin without haptic obstacles has not previously been achieved. SenSkin is also novel in that quantitative tangential force applied to the skin, such as that of the forearm or fingers, is measured. An infrared (IR) reflective sensor is used since its durability and inexpensiveness make it suitable for everyday human sensing purposes. The multiple sensors located on the two armbands allow the tangential and normal force applied to the skin dimension to be sensed. The input command is learned and recognized using a Support Vector Machine (SVM). Finally, we show an application in which this input method is implemented.

When robots serve in urban areas such as shopping malls, they will often be required to approach people in order to initiate service. This paper presents a technique for human-robot interaction that enables a robot to approach people who are passing through an environment. For successful approach, our proposed planner first searches for a target person at public distance zones anticipating his/her future position and behavior. It chooses a person who does not seem busy and can be reached from a frontal direction. Once the robot successfully approaches the person within the social distance zone, it identifies the person's reaction and provides a timely response by coordinating its body orientation. The system was tested in a shopping mall and compared with a simple approaching method. The result demonstrates a significant improvement in approaching performance; the simple method was only 35.1% successful, whereas the proposed technique showed a success rate of 55.9%.

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.

We aim to develop a shopping companion robot that can share experience with users. In this study, we focused on the shared memory acquired when a robot walks together with a user. We developed a computational model of memory recall of visited locations in a shopping mall. The model was developed with data collection from 30 participants. We found that shop size, color intensity of facade, relative visibility, and time elapsed are the influencing features for recall. The model was used in a scenario of a shopping companion robot. The robot, Robovie, autonomously follows a user while inferring the user's memory recall of shops in the visited route. When the user asks the location of other shops, Robovie replied with destination description, referring to the known locations inferred with the model of the user's memory recall. With this scenario, we verified the effectiveness of the developed computational model of memory recall. The evaluation experiment revealed that the model outputs shops that the participants are likely to recall, and makes the directions given easier to understand.

The Turing test is an imitation game for determining the intelligence of an agent. In spite of its simplified setting, the use of natural language between two agents in the test is still too high a hurdle for achieving fruitful results in the field of artificial intelligence. In this paper, the authors propose a variation of the Turing test with a restricted communication method. This modified test uses behaviors generated by two-axis actuators for communication instead of the natural language dialogue used in the normal Turing test. This reduction of scope reveals what kinds of features are essential for an imitation game, and broaden the application brought by Turing test. When we learn what sorts of communication become possible with restricted actuation, we can apply this knowledge to any kind of robot or device in the real world. First, we tried to determine what elements are critical for communication between a user and a robot through a preliminary experiment involving human-human communication. A human manipulator received a video image as input and controlled a "robot box" with two actuators in a way that would lead a user to put other objects into the box. The results indicated what kinds of behavior are required to show the intention of the manipulator to the user. Second, we analyzed the result of the preliminary experiment, organized a behavioral model from the result, and programmed the robot box to run the model. The behavior of the robot was programmed according to the user's head and hand locations as identified by a motion captures system. The robot automatically interact with a human without human manipulation with this program. Third, we conducted a behavioral Turing test in a communication task whereby the human collected items according to the instructions of the robot box. In this test, two actuators on the box is controlled both by human manipulator and our program. The answers of users suggests that the users could not identify which is controlled by a human manipulator or the program. This result indicates that the Turing test succeed in a restricted behavioral level.

What will users want from robotic arms when they become part for their daily lives? We conducted an online survey to collect ideas from people about the “places” in which they would want to use a robotic arm and “tasks” that they would want the robot arm to perform. 96 people anonymously volunteered to participate in this study. More than 240 sets of place and task were identified in the results of two versions of the web-based questionnaires. The results suggest that the household, workplace, and working surface are, respectively, the three most-mentioned places for robotic arm usage in daily life, that participants who were from different countries but were familiar with information technology did not show significant differences in their answers, and that females showed more interest in household and self-care tasks. Our findings can be used as a guideline for future research and development that focuses on daily life tasks for robotic arms.

We present the iRing, an intelligent input ring device developed for measuring finger gestures and external input. iRing recognizes rotation, finger bending, and external force via an infrared (IR) reflection sensor that leverages skin characteristics such as reflectance and softness. Furthermore, iRing allows using a push and stroke input method, which is popular in touch displays. The ring design has potential to be used as a wearable controller because its accessory shape is socially acceptable, easy to install, and safe, and iRing does not require extra devices. We present examples of iRing applications and discuss its validity as an inexpensive wearable interface and as a human sensing device.

This paper proposes a direct anthropomorphization method to improve interaction between human and an agent. In this method, an artifact is converted into an agent by attaching humanoid parts to it. There have been many studies that have provided valu

In this study, we propose a disembodied real-world agent and the study of the influence of this disembodiment on the social separation between the user and the agent. In order to give a clue to the user about the presence of the robot and to make possible a visual feedback, we decide to use independent robotic body parts that mimic human hands and eyes. This robot is also able to share real-world space with the user, and react to his presence, through 3d detection and oral communication. Thus, we can obtain an agent with an important presence while keeping good space efficiency, and as a result ban any existing social barrier.

An unobtrusive measurement tool is important for Human-Robot Interaction (HRI) research that does not want to attach markers or devices on the experiment subjects. This property allows a natural observation of interaction between human and robot during the experiment. In HRI, an experiment that involves with psychological evaluation usually requires physical measurement for evaluating the interaction, for instance, distance, direction, and approaching speed between human and robot. Low-profile Evaluation System (LES) is an ongoing work in creating an unobtrusive measurement tool for HRI research that provides 3D video recording, playback and measurement inside 3D scene in real-time. LES is designed to operate on a single computer to make setup and experiment outside laboratory environment easier. The demonstration in the real-word setup shows that LES provides acceptable 3D measurement accuracy for HRI research.

Unpredictable user behaviors in a physical process represent one of the fundamental obstacles to the realization of a Cyber-Physical System. In this paper, we propose the use of social cues such as body shape, expressions, and verbal timing to control user behaviors in the physical world. Social cues can control user behaviors in both their spatial and temporal aspects. As a result, user actions become more predictable in a CPS. We consider how social cues restrict user behaviors by referring to a number of psychological, cognitive, and human-robot interaction studies, and we propose a model of restriction based on social cues. Using this model, we created hardware and software in order to realize attachable social cues, and we seek to demonstrate the effect of social cues using the example of home appliances.

In daily conversation, we sometimes observe a deictic interaction scene that refers to a region in a space, such as saying "please put it over there" with pointing. How can such an interaction be possible with a robot? Is it enough to simulate people's behaviors, such as utterance and pointing? Instead, we highlight the importance of simulating human cognition. In the first part of our study, we empirically demonstrate the importance of simulating human cognition of regions when a robot engages in a deictic interaction by referring to a region in a space. The experiments indicate that a robot with simulated cognition of regions improves efficiency of its deictic interaction. In the second part, we present a method for a robot to computationally simulate cognition of regions.

In this study, we investigated the boundary for recognizing robots. Many anthropomorphic robots are used for interactions with users. These robots show various body forms and appearances, which are recognized by their users. This ability to recognize a variety of robotic appearances suggests that a user can recognize a wide range of imaginary body forms compared with the native human appearance. We attempted to determine the boundary for the recognition of robot appearances. On the basis of our previous studies, we hypothesized that the discrimination of robot appearances depends of the order of the parts. If the body parts of a robot are placed in order from top to bottom, the user can recognize the assembly as a robot body. We performed a human-robot experiment in which we compared the results for robots with ordered parts with those for robots with inverted parts. The result showed that the users' perception of the robot's body differed between the two groups. This result confirms our hypothesized boundary for the recognition of robot appearances.

A system has been developed that can project an embodied agent's image and sound anywhere in a room. It can thus overcome the problems inherent to other embodied agents in dealing with 2D on-screen information and 3D physical information simultaneously. An experiment demonstrated that this `PROT' agent can effectively present both on-screen information and real-world physical information. Because the PROT agent combines the advantages of a robot agent with those of an on-screen agent, it should improve human-robot interaction.

Dialogue interface with voice is one of the common methods of interaction between a user and a machine. This interface is believed to be easy for all people because auditory information doesn't require additional knowledge from users. However, having only auditory instructions sometimes causes misinterpretation of spatial information like locations and directions. This risk becomes large, especially with older people, because users' mental abilities to manipulate images or patterns decrease with age. We support older people's learning of spatial information by using an attachable gesture robot. Our robot consists of human-like eyes and arms and is attached to the object. It supports older people's mental manipulation of space with gestures during voice interaction. We designed and implemented both hardware and software on a vacuum robot and evaluated the method by training older people to learn its features. We compared two instructional methods that explained eight features on the vacuum, one method with voice and gestures and one with only voice. The results show that the subjects were more likely to remember two features if given training with gestures. We also found that their learning motivation was increased when given voice and gesturing instructional methods.

In this article, we propose a model for the addition of user information to sensor data. The problem is that it is difficult to identify a user of objects that are used by multiple people and to define a unique way to identify the user. The proposed model identifies the user assuming that the objects are used by multiple people. Further, it introduces three policies. By our experiment, we have proved the following: (1) Addition of user information to sensor data by the model is valid. (2) By presenting a difference in policies, it is possible to draw someone's attention.

This paper proposes to share gesture contents among heterogeneous robots. In this paper, we classify gestures in two types; pointing gesture and track gesture. Criteria of classification is factors which are essential for each gesture. Pointing gestures are used for pointing somewhere around a robot. Trajectory of a gesture is important for track gesture. Track gestures can keep its essential factors by moving track horizontally or vertically. We made gesture translation algorithms for each classification and achieved sharing semantic information of gesture contents. We carried out quantitative evaluation with simulations, and qualitative evaluation with questionnaire.

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.

Providing route directions is a complicated interaction. Utterances are combined with gestures and pronounced with appropriate timing. This study proposes a model for a robot that generates route directions by integrating three important crucial elements: utterances, gestures, and timing. Two research questions must be answered in this modeling process. First, is it useful to let robot perform gesture even though the information conveyed by the gesture is given by utterance as well? Second, is it useful to implement the timing at which humans speaks? Many previous studies about the natural behavior of computers and robots have learned from human speakers, such as gestures and speech timing. However, our approach is different from such previous studies. We emphasized the listener's perspective. Gestures were designed based on the usefulness, although we were influenced by the basic structure of human gestures. Timing was not based on how humans speak, but modeled from how they listen. The experimental result demonstrated the effectiveness of our approach, not only for task efficiency but also for perceived naturalness.

With this video, we propose a new human-robot interaction that anthropomorphizes a target common object and transform it into a communicative agent using attachable humanoid parts. The user perceives the target to have its own intentions and body image through the attached body parts. This video shows examples of anthropomorphization method as below.

In this paper, we propose new robotics presentation method called, Self Introducing Poster that uses attachable humanoid parts and explains its contents through a self introduction style. Presentation by a conventional robot sometimes fails because the robot presenter is often too attractive and distracts from the presentation itself. In our method, the poster is anthropomorphized and explains its contents. Due to this self presentation, users can more easily understand its meaning because the information's contents and information provider are strongly related. We designed and implemented our system and evaluated it in the field. The results suggest that the self-introducing system is useful for gaining users attention and effectively presenting information.

This paper proposes a model of approach behavior with which a robot can initiate conversation with people who are walking. We developed the model by learning from the failures in a simplistic approach behavior used in a real shopping mall. Sometimes people were unaware of the robot's presence, even when it spoke to them. Sometimes, people were not sure whether the robot was really trying to start a conversation, and they did not start talking with it even though they displayed interest. To prevent such failures, our model includes the following functions: predicting the walking behavior of people, choosing a target person, planning its approaching path, and nonverbally indicating its intention to initiate a conversation. The approach model was implemented and used in a real shopping mall. The field trial demonstrated that our model significantly improves the robot's performance in initiating conversations.

This paper proposes a robot communication strategy that enables a human's context to be incorporated into a robot's context. The strategy's fundamental principle is that a robot will show awareness of a human's context. In our pilot study, many participants did not actually start interacting with the robot, but tested its functionality. According to the results of that pilot study, we implemented a robot with behaviors that showed awareness of such peculiar human's behaviors. We conducted a field experiment to verify the effectiveness of the robot's behaviors for showing awareness. The results indicated that showing awareness can be a method for involving humans in an interaction with a robot.

This paper reports a study about system response time (SRT) in communication robots that utilize human-like social features, such as anthropomorphic appearance and conversation in natural language. Our research purpose established a design guideline for SRT in communication robots. The first experiment observed user preferences toward different SRTs in interaction with a robot. In other existing user interfaces, faster response is usually preferred. In contrast, our experimental result indicated that user preference for SRT in a communication robot is highest at one second, and user preference ratings level off at two seconds.

This study investigated the effects of a method of anthropomorphizing a target space using attachable robotic human parts. As a result of this method, users may perceive a space as having lifelike characteristics and may accept a virtual body image for various objects. We developed robotic human parts to evaluate our method and conducted an experiment in which subjects positioned objects according to instructions given within an anthropomorphic space to determine their acceptance of the virtual body images presented and the anthropomorphic representation of the space. The results showed that subjects’ positioning of objects changed according to the position of the human parts. We found that our method successfully generated a virtual body image for a particular region that would not normally be recognized in this way by users.

This paper investigates a communication model which explains what behaviors make humans think that they share information with a robot. We focus on the motion of a robot’s gaze which is synchronized with a human’s gesture and a gaze direction, and propose a communication model for a robot to share real world information with a human. We have conducted a psychological experiment and reveals the effect of the simultaneous behaviors. The result indicated that the simultaneous behaviors give actuality to the robot’s utterance when it refers to physical world information.

A new system named Home-Explorer is proposed to search and localize physical artifacts in smart indoor environment. Our view is object-centered and sensors are attached to several objects (named smart objects) in the space. Different from others' research, our system tackles not only smart objects but also hidden objects (e.g. no sensor attached objects). Home-Explorer resolves the hidden object problem by reasoning the physical context from smart objects. A series of inference rules are presented for context reasoning. Moreover, in order to deal with the uncertainty problem when estimating the identity of the hidden objects, we present two effective ways: attribute matching mechanism and associated relation method. Besides, to enhance user-friendly, multiple search modes are provided.

A simple view of deictic communication only includes the indication process and recognition process: a person points at an object and says something about it such as “look at this,” and then the other person recognizes the pointing gesture and pays attention to the indicated object. However, this simple view lacks three important processes: attention synchronization, context focus, and believability establishment. We refer to these three processes as “facilitation processes” and implement them in a humanoid robot with a motion capturing system. An experiment with 30 subjects revealed that the facilitation processes make deictic communication natural.

This paper proposes a new task selection method for multi-task communication robots. As robot technology improves, communication robots are being used in public places. These communication robots are meant to perform patrol, promotion, or guidance activities in place of humans. When a communication robot is implemented for multiple purposes, it is important that the robot be able to appropriately select its own tasks. In this paper, we propose a collaborative task casting method to support communication robots when they are selecting a task. We also describe the implementation of a task management system based on the proposing method. This system takes into account global task achievement and balances task occupation to ensure that adequate robot resources are directed towards each task.

We propose a "display robot" that directly anthropomorphizes objects using body parts that are like those of humans. It is constructed of devices that look like eyes and arms. It anthropomorphizes objects according to the places they are located at or the situations they are in, and this increases their subjectivity and their virtual body image. The display robot enables users to accept the body images of objects and communicate smoothly with them. We assessed user evaluations of the display robot in an exhibition space and in this paper we discuss the effect of anthropomorphization achieved by these devices.

The paper proposes a new middleware named semantic sensor network which connects the variety of physically grounded applications to the information of the real world. Semantic sensor network infers and describes the state of an environment using logical expressions. The target which semantic sensor network describes is an environment where wireless sensor nodes are attached to daily items. The remarkable achievement of semantic sensor network is to employ explicitly the concept of a class and an instance on the sensor network so as to construct efficiently an interpretation model for sensor data and queries. We have developed two physically grounded applications on semantic sensor network; one is a GUI and the other is a robotic system. They use semantic sensor network to obtain information about the environment

This paper presents a three-layer model for generation and recognition of attention-drawing behavior. The model enables a robot to recognize people's attention-drawing behavior as well as to perform attention-drawing behavior to people. It consists of three layers: the PSM (pointing space model), the RTM (reference term model), and the OPM (object property model). The PSM associates the pointing gesture with a reference term, the RTM associates positional relationships with a reference term, and the OPM associates other supplemental verbal cues with a reference term. We implemented the model in a humanoid robot, Robovie, and verified its effectiveness through an experiment

For frequently data arriving data environment, this paper tackles the following three problems. (1) Maximizing throughput. (2) Minimizing logging time. (3) Minimizing blocking time. To solve these problems, this paper proposes Two Level Asynchronous Checkpointing technique. Furthermore this paper designs and implements the technique within DBMS and experiments are conducted by using the DBMS to evaluate the technique. The result of experiments show that remote logging provides better performance than disk logging, 10 times for (1), 17 times for (2). Furthermore, average blocking time is shown as 4.38 micro seconds.

This study proposes a system named "displaying robot" which anthropomorphize an environmental object. Although there are many studies on anthropomorphic agents to communicate with humans about an environmental object, a communication becomes irritating because we need to communicate with agents additionally. On the other hand, displaying robot is attachable device which imitates human body parts for anthropomorphization. Since displaying robot anthropomorphizes an environmental object itself, it can conduct task-related communication without additional anthropomorphic agents. We design one of the displaying robots using two eyes, named "Iris-board" which is attached to home appliances or furniture. We conducted an experiment to verify the effectiveness of a body image with attaching Iris-board to a home appliance. The result indicated that there is a significant difference between anthropomorphized home appliance and normal home appliance

When describing a physical object, we indicate which object by pointing and using reference terms, such as ‘this’ and ‘that’, to inform the listener quickly of an indicated object's location. Therefore, this research proposes using a three-layer attention-drawing model for humanoid robots that incorporates such gestures and verbal cues. The proposed three-layer model consists of three sub-models: the Reference Term Model (RTM); the Limit Distance Model (LDM); and the Object Property Model (OPM). The RTM selects an appropriate reference term for distance, based on a quantitative analysis of human behaviour. The LDM decides whether to use a property of the object, such as colour, as an additional term for distinguishing the object from its neighbours. The OPM determines which property should be used for this additional reference. Based on this concept, an attention-drawing system was developed for a communication robot named ‘Robovie’, and its effectiveness was tested.

When we talk about objects in an environment, we indicate to a listener which object is currently under consideration by using pointing gesture and such reference terms as "this" and "that". Such reference terms play an important role in human interaction by quickly informing the listener of an indicated object's location. In this research, we propose a three-layered draw-attention model for humanoid robots with gestures and verbal cues. Our proposed three-layered model consists of three sub models: reference term model (RTM), limit distance model (LDM) and object property model (OPM). RTM decides an appropriate reference term using functions constructed by an analysis of human behavior. LDM decides whether to use the object's property with a reference term. OPM decides the appropriate property for indicating the object by comparing object properties with each other. We developed an attention drawing system in a communication robot named "Robovie" based on the three layered model. We confirmed its effectiveness through the experiments.

Previous research works in robotics and cognitive science have reported that humans utilize embodied cooperative behaviors in communication, such as nodding in response to another's it utterance and looking at a certain object in a certain direction as others look or point at. We have developed a humanoid robot that utilizes such an embodied cooperative behavior for natural communication in a route guidance situation. It obtains numerical data on a human's body movement via a motion capturing system and then autonomously selects appropriate cooperative embodiment units from 18 implemented units. Each unit realizes a certain cooperative embodiment behaviors such as eye-contact by using the motion capturing system as well. As a result of a subject experiment, we have verified the effectiveness of the embodied cooperative behaviors of the robot for reliable and sympathetic communication. Moreover, we analyzed how the auditory expression and the embodiment contributed to the effect.

We report the development and evaluation of a new interactive humanoid robot that communicates with humans and is designed to participate in human society as a partner. A human-like body will provide an abundance of nonverbal information and enable us to smoothly communicate with the robot. To achieve this, we developed a humanoid robot that autonomously interacts with humans by speaking and gesturing. Interaction achieved through a large number of interactive behaviors, which are developed by using a visualizing tool for understanding the developed complex system. Each interactive behavior is designed by using knowledge obtained through cognitive experiments and implemented by using situated recognition. The robot is used as a testbed for studying embodied communication. Our strategy is to analyze human-robot interaction in terms of body movements using a motion-capturing system that allows us to measure the body movements in detail. We performed experiments to compare the body movements with subjective evaluation based on a psychological method. The results reveal the importance of well-coordinated behaviors as well as the performance of the developed interactive behaviors and suggest a new analytical approach to human-robot interaction.

This paper proposes a network system and an algorithm for a rescue robot to obtain its position under collapsed area. The network system consists of communication tags put dynamically by the rescue robot in its rescue activities. According to the temporary tags, the system constructs temporary communication infrastructure and obtains geometrical information of the area. In particular, to get the position of the rescue robot, our algorithm employs "angle" obtained from Omni-directional Sensor mounted on the communication tag. The use of the "angle" information leads a significant decrease in the error in estimating tags' location. In this paper, the feasibility of our system and algorithm is confirmed with the simulation.

This study investigated what effect physical constraints have on the interpretation of demonstrative pronouns when a user navigates a robot. For this investigation, a robot navigation environment called Spondia-II was develope, and an experiment conducted. It is known that the interpretation of demonstrative pronouns requires information about not only the situation (or context) but also the speaker's viewpoint during a dialogue. The results of the experiment suggest that physical constraints do affect the user's viewpoint, especially when a user utters a demonstrative pronoun while navigating the robot. In actual fact, the user alters the use of demonstrative pronouns according to the change in the user's viewpoint. It is also suggested that the user and the robot share the same viewpoint during the physical interaction.

In this paper, we report about a new interaction-oriented robot, which communicates with humans and will participate in human society as our partner. For realizing such a robot, we have started a new collaborative work between cognitive science and robotics. In the way of robotics, we have developed a humanoid robot named "Robovie" that has enough physical expression ability. On the other hand, through cognitive experiments, we obtained important ideas about the robot's body property. To incorporate these ideas, we have developed software architecture and implemented autonomous interactive behaviors to the robot. Further, we have evaluated the robot's performance of the interactive behaviors through psychological experiments. The experiments reveal how humans recognize the robot.

There is a strong correlation between the number of appropriate behaviors an interactive robot can produce and its perceived intelligence. We propose a robot architecture for implementing a large number of behaviors and a visualizing tool for understanding the developed complex system. Behaviors are designed by using knowledge obtained through cognitive experiments and implemented by using situated recognition. By representing relationships between behaviors, episode rules help to guide the robot in communicating with people in a consistent manner. We have implemented over 100 behaviors and 800 episode rules in a humanoid robot. As a result, the robot could entice people to relate to it interpersonally. An Episode Editor is a tool to support the development of episode rules and to visualize the complex relationships among the behaviors. We consider the visualization is to be necessary for the constructive approach.

Sensor database systems need to provide both freshness of data and persistence to the incoming sensor streams. To provide persistence, a disk based logging method has been widely used, however it is not applicable for sensor streams because of its tardiness. In this paper, we propose the light neighbor write ahead logging protocol(L-WAL) for sensor streams. The L-WAL is a refinement of the neighbor-WAL (N-WAL). The L-WAL needs two network interfaces and applies a relaxed protocol rather than a two phase commit protocol. Since the relaxed protocol weakens the guarantee of logging successes, we have incorporated a repair system and checker system to enhance the guarantee. The result of experiments shows that the L-WAL is about 2.13 times faster than the N-WAL when the number of concurrent sensor streams is 250 and the L-WAL can enhance the persistence of data almost for free, while the N-WAL needs to pay high cost.

InterSpace is a revolutionary communication environment that allows users the flexibility of multi-modal interaction. People in InterSpace communicate using audio as well as video interaction in a three dimensional world. Remote terimals are connected to a central server via networks. Facial image, audio, and proximity, are processed and sent out to the remote terminals to enable multi-modal communication in a virtual world. InterSpace technology comes a step closer to bridging the gap between virtual reality and world experiences. We conducted a trial service, CyberCampus, based on the InterSpace platform. Individual actions can now be shared with other users as you explore, talk, shop, learn, and experience the many facets of CyberCampus. Environments related to entertainment, distance learning, on-line shopping, and advertisement are currently being explored in CyberCampus with unlimited expansion capabilities.