To provide more services on multiple floors in buildings, recent mobile robots have the capability to take elevators and need to board them with other passengers. When a mobile robot boards an elevator, the current technological limitations result in stress for the surrounding passengers. However, it is still unclear what impressions a robot’s quick boarding behavior will impart to surrounding passengers, when such limitations are removed in the future and mobile robots acquire human-like quick movements. It is also difficult to increase the speed of robots drastically in a manner which ensures the safety of experiment participants. In this study, we developed a mixed reality system that integrates a virtual robot into the real world to ensure the physical safety of participants during the experiment. Visual and auditory information about the virtual robot was provided to participants through a head-mounted display and was synchronized with the robot’s behavior. We focused on three basic factors of robot’s boarding behavior: forward speed during entry, rotation speed, and rotation direction in the elevator car. A statistical analysis revealed that quick, human-like entry into an elevator by a virtual robot reduces perceived anthropomorphism, likeability, intelligence, and safety, and increases the stress levels experienced by the passengers. The statistical analysis also showed that quick rotation inside the elevator by the virtual robot reduces perceived likeability. Our results provide useful knowledge for designing the behavior of future robots when they board elevators with other passengers.

Cybernetic avatar (CA) is an emerging technology with increasing popularity. Since CAs can be teleoperated remotely, the performance and comfort of CA operators are crucial. In this study, three different viewpoint types were evaluated: first-person view, behind-avatar view, and third-person view. In our experiment, a head-mounted display (HMD) was used to display simulated video images to the participants and track their behavior. We investigated which viewpoint type is most suitable for operators to increase usability and reduce fatigue. Participants were asked to answer a questionnaire to evaluate their satisfaction after each experimental trial. Their gaze and head movements were also analyzed. The questionnaire results suggest that the best viewing type is the behind-avatar view and the worst viewing type is the third-person view. We also found a correlation between each question and gaze movement. Our results reveal that rapid eye movements are correlated with the answer from the questionnaire. In summary, participants considered the system difficult to use and felt tired if there were many rapid eye movements in an experimental trial.

Recent mobile robots have the capability to take elevators from different floors in buildings. Although various technologies enable robots to use elevators for their daily environments, past research works mainly focused on the side of mobile robots, e.g., social behaviors through the robot’s device. In other words, the effects of social behaviors from smart elevators and the collaborative situations between mobile robots and elevators have received inadequate attention. Therefore, in this study, we investigate the effects of interaction design for a mobile robot and a smart elevator when the former takes the latter. We conducted an experiment where a mobile robot and/or an elevator spoke to passengers as voice cues and evaluated their perceived impressions toward the robot and the elevator. The experiment results showed positive effects of the voice cues of the smart elevator and the mobile robot, although we found no combination effects of both voice cues. On the other hand, either voice cue creates a positive impression toward the other, i.e., the elevator voice cue improves the perceived impressions of the mobile robot and vice versa. This phenomenon provides useful knowledge for both elevator/robot developers.

Robots deployed in large human-populated indoor environments such as shopping malls, airports etc., inadvertently communicate via wireless networks for enhanced perception and decision making capabilities. Owing to highly dynamic signal attenuation characteristics in such environments, connectivity issues may arise during robotic navigation, leading to disruption in information flow causing potential danger. Exact modeling of signal propagation for estimating spatial signal variation is usually challenging. Moreover, the presence of dynamic humans also add a layer of temporal signal variation complexities. Thus, this paper introduces a generative approach for embedding radio signal strength constraints within networked service/social robot navigation in large human-populated environments. Initially, we propose a Gaussian Process based online spatio-temporal signal strength prediction model that, as opposed to the current state of the art, also aims to take into account the temporal fading arising due to the presence of human crowds. We then devise a transient-goal driven navigation strategy to realize a sub-optimal path towards a goal, that is aimed at resolving both communication-aware and human-aware planning constraints. Evaluations of the proposed signal prediction model demonstrate the advantages of our approach with respect to the current state of the art. The efficacy of the navigation strategy in also demonstrated simulations and using hardware experiments conducted on a robotic wheelchair operating in a large shopping mall.

In this paper, we lay the algorithmic foundations of a unifying strategy for pedestrian-aware navigation that is aimed at service/social robots deployed in large human-crowded environments. In order to accommodate both modeled and learned social navigation behaviors, we formalize an approach within which the robot traverses to a specific goal (or sub-goal) via a trajectory of optimal transient-goals or optimal short-term way-points. We then evaluate an implementation of the navigation strategy, by utilizing an augmented Risk-based Rapidly Exploring Random Trees (RRT) planner, and demonstrate its efficacy for real-world deployment using discriminative simulations and by providing avenues for future work.

The use of 3D range sensors for human position tracking has grown in recent years, especially for augmenting robotic sensing for human-robot interaction. However, extrinsic calibration of the relative positions of 3D range sensors is difficult, due to their limited range, narrow field of view, and distortion at large distances. 2D laser range finders have also been used for pedestrian tracking, providing greater accuracy and coverage at the cost of being more expensive and susceptible to occlusion. In this work, we present two novel techniques for calibrating the positions of 3D range sensors based on shared observations of pedestrians. The first technique uses 3D range sensors alone, and the second technique uses 2D and 3D range sensors together, using the high precision and long range of the 2D sensors to complement the short-range but richer sensing of 3D range sensors. We evaluate the accuracy of both automatic calibration techniques, and we furthermore show that the combination of 2D and 3D sensors gives more robust and accurate calibration than when using 3D sensors alone.

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

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

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

The noninvasive brain-machine interface (BMI) is anticipated to be an effective tool of communication not only in laboratory settings but also in our daily livings. The direct communication channel created by BMI can assist aging societies and the handicapped and improve human welfare. In this paper we propose and experiment a BMI framework that combines BMI with a robotic house and autonomous robotic wheelchair. Autonomous navigation is achieved by placing waypoints within the house and, from the user side, the user performs BMI to give commands to the house and wheelchair. The waypoint framework can offer essential services to the user with an effectively improved information-transfer rate and is an excellent examples of the fusion of data measured by sensors in the house, which can offer insight into further studies.

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

In this paper, we discuss how networked robot architectures can facilitate the development, deployment, management and adaptation of distributed robotic applications. Our aim is to modularize applications by factoring out environment, task-, domain-, and robot-specific knowledge components and representing them explicitly in a formal knowledge base that is shared between the robots and service applications. Robot control decisions can then be formulated in terms of inference tasks that are evaluated based on this knowledge during task execution. The explicit and modular knowledge representation allows human operators with different areas of expertise to adapt the respective parts of the knowledge independently. We implemented this concept by integrating knowledge representation methods of the RoboEarth project with the distributed task execution capabilities of the Ubiquitous Network Robot Platform.

A method for tracking the position, orientation, and height of persons in large public environments is presented. Such a piece of information is known to be useful both for understanding their actions, as well as for applications such as human-robot interaction. We use multiple 3-D range sensors, which are mounted above human height to have less occlusion between persons. A computationally simple-tracking method is proposed that works on single sensor data and combines multiple sensors so that large areas can be covered with a minimum number of sensors. Moreover, it can work with different sensor types and is robust to the imperfect sensor measurements; therefore, it is possible to combine currently available 3-D range sensor solutions to achieve tracking in wide public spaces. The method was implemented in a shopping center environment, and it was shown that good tracking performance can be achieved.

Parkinson's disease (PD) is a severe disease with many symptoms, including speech disorders. Although many methods exist to treat some of PD's symptoms, therapies for speech impairment are not effective and satisfactory, resulting in an open area of research. The current project aims at taking advantage of the Lombard reflex to improve the speech loudness of PD patients. As a first step, the experience of the Lombard reflex by Japanese PD people was confirmed, and the perception of PD patients' speech was evaluated by several subjects. In a following step, methods based on masking sound will be used for intensive training and for self-training of PD patients. However, after intensive training, PD patients may be able to talk louder even without masking noise. In addition, the design and the development of a device based on masking sound that can be used by PD patients while using phone is under consideration.

This paper focuses on the problem of environmental noises in human-human communication and in automatic speech recognition. To deal with this problem, the use of alternative acoustic sensors -which are attached to the talker and receive the uttered speech through skin or bones- is investigated. In the current study, throat microphones and ear bone microphones are integrated with standard microphones using several fusion methods. The results obtained show that the recognition rates in noisy environments are drastically increased when these sensors are integrated with standard microphones. Moreover, the system does not show any recognition degradations in clean environments. In fact, recognition rates also increase slightly in clean environments. Using late fusion to integrate a throat microphone, an ear bone microphone, and a standard microphone, we achieved a 44% relative improvement in recognition rate in a noisy environment and a 24% relative improvement in recognition rate in a clean environment.

This paper presents an audio monitoring system for detecting and identifying people engaged in a conversation. The proposed method is hands-free as it uses a microphone array to acquire the sound. A particularity of the approach is the use of a laser range finder based human tracker system. The human tracker monitors the locations of people then local steered response power is used to detect the people speaking and localize precisely their mouths. Then an audio stream is created for each person and used to perform speaker identification. Experimental results show that the use of the human tracker has several benefits compared to an audio only approach.

This study focuses on joint motion patterns of humans that move together with other humans or objects. Since this scope embraces `group motion', which relates only humans, and expands its extent of interactions accounting for various auxiliary instruments such as walking aids or pushcarts, we term this collective motion pattern as `coherent' motion. Coherence is proposed to be characterized by the distance between the moving parties, the scalar product of their velocities and the scalar product of the velocity vector and the displacement vector. The contribution of this study lies in the formulation of coherence in terms of the listed features through explicit mathematical models. The models are developed in accordance with a large database recorded in an uncontrolled environment involving a total of more than 500 mobile entities. The performance of the proposed models is evaluated qualitatively by comparing them to the empirical data and quantitatively by employing log-likelihoods. Comparison to an earlier work indicates that the proposed models improve the identification of coherence quality significantly well.

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

Recently, laser-based people-tracking systems have received increasing attention for their ability to localize human subjects precisely in crowded situations. However, in a real environment, there exist many kinds of moving objects other than people, and previous methods have focused only on humans. To design a more sophisticated system, it is necessary to distinguish humans from observed objects and recognize their individual condition-for example, the kind and amount of belongings they are carrying. However, in previous methods using 2D laser range finders (LRFs), it proved difficult to recognize the type of target since all sensors observe a common horizontal plane and only the 2D contours of their targets. In this study, to recognize the type of target, we observe the 3D shapes of objects moving in their environment by installing LRFs with an angle of inclination. So far, in the area of 3D modeling, LRFs have been used to construct 3D models of static objects by moving the sensor and registering multiple views. In contrast, our method observes moving objects by using a static LRF network in the environment. Experimental results are shown to confirm the effectiveness of the proposed method.

This paper proposes a simple yet novel method for recognition of certain sorts of moving entities incorporating their shape and motion patterns. Although shape features have been commonly employed in object recognition, motion characteristics are in general not integrated to geometric models. In the interest of utilizing the motion attributes, the trajectories are investigated to extract the `coherence quality' of the entities. Besides, at every step a geometric shape model is adopted and the parameters defining the shape model are utilized in obtaining the prior probabilities of the entities being a member of a particular class of interest. The coherence quality is used to get the posterior probabilities through a Bayesian approach. The main contribution of this paper is the incorporation of coherence quality in identification of moving entities. The proposed method is tested against clutter and occlusion in an uncontrolled environment with patterns collected from over 500 entities. It is shown to yield a satisfactory performance rate of 92% over the entire dataset with significant generalization capabilities without any restrictions on the application setting and with considerable occlusion and clutter.

To provide personal and location-dependent services in public spaces such as shopping malls, it is important to be able to estimate the positions and identities of people in the environment. Sensors in the environment reliably detect their current positions, but it is difficult to identify people using these sensors. On the other hand, signals from wearable sensors can be used to identify people correctly, but precise position estimation remains problematic. In this paper, we describe a novel method of integrating laser range finders (LRFs) in the environment and wearable inertial sensors. Time sequences of angular velocities estimated from both LRFs and wearable sensors are matched to identify people. Examples of tracking individuals in the environment that confirm the effectiveness of this method are shown.

Laser range finders are a non-invasive tool which can be used for anonymously tracking the motion of people and robots in real-world environments with high accuracy. Based on a commercial system we have developed, this paper addresses two practical issues of using networks of portable laser range finders in field environments. We first describe a technique for automated calibration of sensor positions and orientations, by using velocity-based matching of observed human trajectories to define constraints between the sensors. We then propose a mechanism for detecting when a sensor has been moved out of alignment, which can be used to alert an operator of the condition and automatically exclude erroneous data from tracking calculations. After describing our techniques for solving these problems, we demonstrate the effectiveness of our calibration and error detection systems in live trials with our real-time system, as well as offline tests based on scan data recorded from field trials.

In this paper we present a new robotic palpation method to perform quantitative measurement of the material parameters of human tissues, for use in medical applications. The proposed method is achieved by the use of a system that integrates a robotic component and a numerical simulation component. The robotic component is used to measure the contact force and displacement at each point on the human body contacted by a robotic probe. The numerical simulation component identifies the material parameters using the proposed method, where two data sources are used, namely, (1) the measured data from the robotic part, and (2) simulated deformation data obtained by the finite element method. In order to validate the proposed system, we report initial results from several phantom tissue experiments, which demonstrate the ability of the system to quantitatively determine the elastic moduli of tissues. We also discuss several potential challenges in the future of the proposed system.

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

For a person to feel comfortable when approaching a robot it is necessary for the robot to behave in an expected way. Peoplepsilas behavior around a robot not being aware of them were observed during a preliminary experiment. Based on those observations people were classified into four groups depending on their interest in the robot. People were tracked with a laser range finder based system, and their positions, directions and velocities were estimated. A second classification based on that information was made and the relation between the two classifications were mapped. Different actions were created for the robot to be able to react naturally to different human behaviors. In this paper we evaluate three different robot behaviors with respect to how natural they appear. One behavior that actively tries to engage people, one that passively indicates that people have been noticed and a third that makes random gestures. During an experiment test subjects were instructed to act according to the groups from the classification based on interest, and the robotpsilas performance with regard to naturalness was evaluated. Both first and third person evaluation made clear that the active and passive behavior were considered equally natural, while a robot randomly making gestures was considered much less natural.

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

This paper proposes a new server platform for supporting various robotic services that interact with people. Every robot serving people in public spaces need to know: where the person is, what the person is doing, and what is happening at the place. The proposed platform is the first integrated system that can robustly measure, recognize and provide information on human activities and spatial nature in the environment, in a standard way. In the shopping mall experiment, the system is able to track and capture activities of more than 20 people simultaneously in real time. We also describe the result of robotic service experiment utilizing the power of this platform.

Robots that are supposed to work and communicate with humans in our daily life have been developed in recent years. For this kind of robots, sensitive tactile sensors are necessary to achieve haptic interactions. However, influences of its own motion on tactile sensors sometimes become larger than the signal to be detected. This paper proposes a method to reduce such noises. It is enabled by estimating and subtracting the influences from sensor outputs. The influences of robotpsilas motions on tactile sensors are estimated by using the sequence of joint angles of the robot. In our method, a partially linear model is built to estimate the influences on a tactile sensor. The robotpsilas posture space, which is represented by its joint angles, is divided into several subspaces to fit to a linear model. We conducted an experiment with a robot covered with tactile sensors to verify the validity of our method. This paper shows that the robot is able to estimate the influences on a tactile sensor in high accuracy by the model.

Identifying the extent to which the appearance of a humanoid robot affects human behavior toward it is important. We compared participant impressions of and behaviors toward two real humanoid robots in simple human-robot interactions. These two robots, which have different appearances but are controlled to perform the same recorded utterances and motions, are adjusted by a motion-capturing system. We conducted an experiment with 48 human participants who individually interacted with the two robots and also with a human for reference. The results revealed that different appearances did not affect participant verbal behaviors, but they did affect such nonverbal behaviors as distance and delay of response. These differences are explained by two factors: impressions and attributions.

We have developed a new communication robot, Robopal, which is an indoor/outdoor robot for use in human-robot interaction research in the context of daily life. Robopal's intended applications involve leading and/or following a human to a destination. Preliminary experiments have been conducted to study nonverbal cues associated with leading and following behavior, and it has been observed that some behaviors, such as glancing towards the leader or follower, appear to be role-dependent. A system for representing these behaviors with a state transition model is described, based on four kinds of interaction roles: directive, responsive, collaborative, and independent. It is proposed that behavior modeling can be simplified by using this system to represent changes in the roles the robot and human play in an interaction, and by associating appropriate behaviors to each role

In this paper we present a method for determining body orientation and pose information from laser scanner data using particle filtering with an adaptive modeling algorithm. A parametric human shape model is recursively updated to fit observed data after each resampling step of the particle filter. This updated model is then used in the likelihood estimation step for the following iteration. This method has been implemented and tested by using a network of laser range finders to observe human subjects in a variety of interactions. We present results illustrating that our method can closely track torso and arm movements even with noisy and incomplete sensor data, and we show examples of body language primitives that can be observed from this orientation and positioning information.

The distance between robots and users increasingly shrinks and with it the potential for robots to inadvertently harm users increasingly grows. Robots need to detect ongoing haptic interaction with users via malleable skin with flexible tactile sensors covering the whole body surface in order to reduce the risk and to realize enriched interactions. In this paper, we propose a feature space based on cross-correlation between tactile sensors, and we visualized it as a 2-dimensional map which we call Somatosensory Map. We construct a haptic interaction database using scenario based haptic interaction between humans and a robot that we equipped with distributed tactile sensors covering the whole body. In the Somatosensory Map, highly correlated sensors make clusters corresponding to distinctive haptic interactions between the robot and the subject. Each haptic interaction can thus be classified using cross-correlation between tactile sensors. To confirm the validity of our proposal, we apply the K-Nearest Neighbor (KNN) method using the feature space. As a validation test result, despite KNN being one of the simplest methods, over 60% of haptic interaction in which we may expect subject’s touches can be recognized. In conclusion, the cross-correlation includes good features to classify the interaction. As future work, the classifier should be improved to use effective cross-correlation shown as distribution of distinctive clusters in the Somatosensory Map.

We believe that robots will take an active part in our daily lives in the near future. Once this time arrives, robots will be expected to have the social skills necessary for interacting with people in addition to the ability to carry out their own tasks. We call an interaction that increases familiarity and makes communication smoother a "social interaction." We have recently developed a human-size humanoid, called "Robovie-IV," which will have the ability to interact with people in their daily lives. This paper discusses the design requirements for humanrobot interaction by a communication robot, then introduces an overview of Robovie-IV's hardware and software architectures. There are also preliminary results of a daily experiment that we are currently conducting with Robovie-IV in our office.

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

In this paper, we propose natural reflexive behaviors for wheeled inverted pendulum type humanoid robots and show that such behaviors change human impressions of the robot. To achieve human-like communication between humans and robots, the latter need to be recognized by the former as communication partners. We believe that the natural reflexive behaviors of robots play an important role in being human partners. To validate the evidence, we generate four types of reflexive robot behaviors when pushed by someone and conducted preliminary experiments with subjects. We verified that people change their impressions of extroversion, agreeableness, intellect, and neuroticism for the robot by changing the reflexive behaviors

It is important to identify how much the appearance of a humanoid robot affects human behaviors toward it. We compared participants' impressions of and behaviors toward two real humanoid robots in simple human-robot interaction. These two robots have different appearances but are controlled to perform the same recorded utterances and motions, which are adjusted by using a motion capturing system. We conducted an experiment where 48 human participants participated. In the experiment, participants interacted with the two robots one by one and also with a human as a reference. As a result, we found that the different appearances did not affect the participants' verbal behaviors but did affect their non-verbal behaviors such as distance and delay of response. These differences are explained by two factors, impressions and attributions.

One major design goal in human-robot interaction is that the robots behave in an intelligent manner, preferably in a similar way as humans. This constraint must also be taken into consideration when the navigation system for the platform is developed. However, research in human-robot interaction is often restricted to other components of the system including gestures, manipulation, and speech. On the other hand, research for mobile robot navigation focuses primarily on the task of reaching a certain goal point in an environment. We believe that these two problems can not be treated separately for a personal robot that coexists with humans in the same surrounding. Persons move constantly while they are interacting with each other. Hence, also a robot should do that, which poses constraints on the navigation system. This type of navigation is the focus of this paper. Methods have been developed for a robot to join a group of people engaged in a conversation. Preliminary results show that the platform's moving patterns are very similar to the ones of the persons. Moreover, this dynamic interaction has been judged naturally by the test subjects, which greatly increases the perceived intelligence of the robot.

This paper reports progress in the development of a humanoid robot designed for real-time face-to-face interaction with humans. An essential component for face-to-face interaction with humans is being able to find faces, tracking them, and smoothly moving back and forth between gazing to a face and other objects of interest. In this paper we propose a system that integrates peripheral vision and foveal vision in a principled manner using particle filters. The developed system generates hypotheses about face position by using peripheral vision and verifies them by integrating peripheral vision and foveal vision. Even though a face may not be present in foveal vision, while the robot is gazing at another object, it keeps plausible hypotheses about the location of the human face with peripheral vision and restarts the face-following by verifying the hypothesis later. Moreover, this fundamental function provides more rich sensory information for human-robot interaction, such as a human's facial expression and lip motions during utterances.

Presents a hybrid structure of reflective gait control and adaptive visual servoing by which a vision guided legged robot realizes a reflective walk. The reflective gait consists of three steps: 1) select a leg to be lifted so as to increase the body stability, 2) move one of other legs to enable the selected leg lifted, and 3) move the selected leg. During these steps, adaptive visual servoing generates a swaying motion of the robot so that it can stabilize the visual target at the desired position in the image. Combining the reflective gait and the swaying, the robot attempts to track the visual target, and as a result a reflective walk emerges. The validity of the method is shown by computer simulation and a preliminary real experiment, and future work is described.

This paper describes a method to achieve a vision-cued swaying task in unknown environments utilizing adaptive visual servoing. The proposed method has a hybrid structure consisting of a controller to keep the distances between feet constant (a stance servoing controller), and an adaptive visual servoing controller. Making use of the method, the motion of each joint need not be pre-programmed, but is generated by the method according to the motion of visual cues. An experimental result demonstrates how the proposed method realizes a vision-cued swaying behavior of the legged robot.

This paper presents an environmental representation, called T-Net (Target Network), for robot navigation. This representation navigates a mobile robot to move in real time in an unstructured environment. The robot approximates local areas with straight paths and memorizes the paths with pairs of feature points. The T-Net consisting of the paths represents rigid environmental structure and keeps relations between the local areas. By tracking the feature points, the robot can move along the T-Net and investigate the environment in detail. The authors have applied this idea to their mobile robot which has multiple vision agents, and verified it through simulations and experimentations in a real world.

This paper presents a strategy of a vision-guided mobile robot for making models of an unknown environment by panoramic sensing. Before moving in the environment, the robot looks around and finds an outline structure of the local environment as a reference frame for building the models. Then the robot builds the models, which are qualitative, while moving along the local structure. We have implemented the robot behaviors into a mobile robot with multiple vision agents which simultaneously execute different vision tasks needed for the panoramic sensing.>