Telexistence technology enables the manipulation of a remote robot using sensory feedback and leader-follower control, giving the operator the impression of controlling their own body. However, the robot’s operability declines significantly due to increasing communication time delays. In order to address this issue, the "Predictive Display" technique has been proposed and developed. This technique displays both the real robot’s movements with time delay and the computer-generated (CG) robot’s movements without time delay. In the Telexistence robot operation system, a predictive display system can be constructed using a virtual reality (VR) system. In this study, we have developed a VR system for Telexistence operation with THK’s SEED-Noid humanoid robot. Unity is utilized, which enables the predictive display function for the stereo camera mounted on the SEED-Noid. We developed and evaluated a display mode without neck joint angle restrictions to reduce VR sickness and workload using the developed VR system. Additionally, we developed a display mode with a limited neck joint angle for comparison purposes.

Self-body awareness refers to the recognition of one's body as one's own and consists of two senses: “sense of body ownership” and “sense of agency.” In telexistence/telepresence robot operation, time delays in the robot's motion degrade self-body awareness of the robot body. We investigated how self-body recognition can be affected in a telexistence robot operation in a VR space when the robot is presented with a real robot arm that simulates a real robot with a delay and a virtual robot arm, or several virtual robot arms, with a delay less than that of the real robot. These experimental conditions include a ‘Predictive Display,’ which is well known as a time delay countermeasure. The results suggest that virtual robot arms presented consecutively with less delay than a real robot arm do not induce a sense of body ownership to the real robot arm, but they enhance the sense of agency to the real robot arm, and that sense of agency is stronger when the task requires precision.

With the aging population trends in developed countries, there is an increased expectation of the application of assistive robots. A variety of robots have been developed to address this challenge. Although there are many technical problems to be solved, the lack of objective evaluation indexes for evaluating the benefit to the users is one of the important problems to be addressed before the commercialization of the assistive robots can be realized. The ICF (International Classification of Functioning, Disability and Health) is a classification of the health components of functioning and disability, and is being utilized in medical, healthcare, and welfare fields. The ICF has a basic framework to evaluate healthcare and welfare equipment, including robots, and it will be the basis of the evaluation indexes for assistive robots. In this paper, we present the development of evaluation indexes, and provide the results of preliminary experiments which were conducted using iARM and RAPUDA robots performing a certain lifting task. The experiments illustrate the issues involved in the development of ICF-based indexes for assitive robots.

Performing general human behavior by experts' navigation is expected to be realized as wearable and ubiquitous technologies and computing develop. For simple, ordinary behavior, a person does not need the assistance of an expert. However, if one is standing next to an injured/ill person, one needs the instruction on performing first aid treatment from an expert. The wearer of the wearable behavior navigation system will be able to conduct first aid treatment as an expert would. We have developed the wearable behavior navigation systems using Augmented Reality technology, mainly for the navigation of the first aid treatment and for escape from dangerous areas, such as a building on fire. The effectiveness of the wearable navigation systems has been evaluated by a number of experiments. In this paper, the basic mechanism to realize general human behavior navigation is presented, along with the concrete configuration of the prototype of the navigation systems, and the experimental evaluation.

With advances in wearable computing, wearable virtual reality and wearable robotics have become popular areas of research. In the present study, we consider the case in which an expert in first aid treatment at a remote critical care center receives an image from an assistant/cooperator who is with a patient, i.e., the sharing of visual information between an expert and an assistant. A head-mounted camera worn by the assistant is used to capture visual information related to the patient and his/her surroundings. The expert directs the assistant to provide the desired visual information. Information of the status of the patient, the surrounding environment, and the situation can be obtained from the shared visual information. The assistant should make observations according to the directions of the expert. However, these persons are not required to be tightly linked/synchronized at all times, but only when necessary. There are primarily two behaviors in visual information sharing, namely, searching and staring behaviors. The proposed asynchronous visual information sharing system with image stabilization can separate these two behaviors and can reduce the stress of both the expert and the assistant in visual information sharing. This system consists of three components: image stabilization, virtual viewing direction operation, and relative orientation display. We experimentally evaluate the performance of virtual viewing direction operation, image stabilization, and asynchronous visual information sharing of the proposed system.

The capability to perform specific human tasks with the assistance of expert navigation is expected to be realized through the development wearable and ubiquitous computing technology. For instance, when an injured or ill person requires first-aid treatment, but only non-experts are nearby, instruction from an expert at a remote site is necessary. A behavior navigation system will allow the user to provide first-aid treatment in the same manner as an expert. Focusing on first-aid treatment, we have proposed and developed a prototype wearable behavior navigation system (WBNS) that uses augmented reality (AR) technology. This prototype WBNS has been evaluated in experiments, in which participants wore the prototype and successfully administered various first-aid treatments. Although the effectiveness of the WBNS has been confirmed, many challenges must be addressed to commercialize the system. The head-mounted displays (HMDs) used in the WBNS have a number of drawbacks, for example, high cost (which is not expected to decrease in the near future) and the time required for an ordinary user to become accustomed to the display. Furthermore, some individuals may experience motion sickness wearing the HMD. We expect that these drawbacks to the current technology will be resolved in the future; meanwhile, a near-future remote behavior navigation system (RBNS) is necessary. Accordingly, we have developed RBNSs for first-aid treatment using off-the-shelf components, in addition to the WBNS. In this paper, the basic mechanisms of the RBNS, experiments investigating the demonstration of expert behavior, and a comparative study of the WBNS and the RBNSs are presented.

As devices such as computers, sensors and motors have decreased in size, wearable computing has become an increasingly active area of research. Guidance of general human behavior by expert navigation is expected to be realized through the development of wearable computing. We aim to apply this technology to first-aid treatment. If an injured or ill person requires first-aid treatment but only non-experts are nearby, guidance from an expert at a remote site would be necessary. We focus on the sharing of visual information between a first-aid expert at a remote critical care center and a non-expert at the patient's location. Visual information about the remote site is captured by a head-mounted camera worn by the assistant and is shared with the expert, thus providing rich information on the status of the patient, the environment and the situation. In a typical master-slave configuration, the shared visual information is tightly linked between the expert and the assistant; that is, the assistant views what the expert wants to see. However, a tight link or synchronization is required only in certain situations, rather than at all times. The aim of this research is to develop a virtual viewing direction operation system with image stabilization for asynchronous visual information sharing between an expert and an assistant. The proposed system consists of three main parts: image stabilization, virtual viewing direction operation and the relative orientation display. The effectiveness of the developed system was confirmed experimentally.

Artificial neural networks have been traditionally employed to learn and compute the inverse kinematics of a robotic arm. However, the inverse kinematics model of a typical robotic arm with joint limits is a multi-valued and discontinuous function. Because it is difficult for a multilayer neural network to approximate this type of function, an accurate inverse kinematics model cannot be obtained by using a single neural network. In order to overcome the difficulties of inverse kinematics learning, we propose a novel modular neural network system that consists of a number of expert modules, where each expert approximates a continuous part of the inverse kinematics function. The proposed system selects one appropriate expert whose output minimizes the expected position/orientation error of the end-effector of the arm. The system can learn a precise inverse kinematics model of a robotic arm with equal or more degrees of freedom than that of its end-effector. However, there are robotic arms with fewer degrees of freedom, where the system cannot learn their precise inverse kinematics model. We have adopted a modified Gauss-Newton method for finding the least-squares solution to address this issue. Through the modifications presented in this paper, the improved modular neural network system can obtain a precise inverse kinematics model of a general robotic arm.

Stray light components in images obtained by the shortwave infrared (SWIR) and visible near-infrared (VNIR) radiometers of the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) were investigated. A simple method, which is equivalent to the van Cittert method of deconvolution, was used for correction. The stray light components were estimated using the image obtained during lunar observation, and the improvement in image quality was examined after stray light correction. The calculation is performed in the space domain, and application to the filter scratch problem of the ASTER/SWIR sensor, which has a scratch on the interference filter resulting in partially degenerated images, is also demonstrated.

An essential factor in understanding the motor learning capability of humans, is the coordinate transformation learning of the visual feedback controller. Although a number of learning models for the visual feedback controller have been proposed, none has been able to establish a definitive approach. In our previous work, we have suggested a learning model that uses disturbance noise and the feedback error signal to learn the human visual feedback controller's coordinate transformation. However, the model does not fully consider the time delay in the visual feedback control loop. This paper presents a modified learning model taking into account the time delay and the convergence properties of the model. Numerical simulations are presented to illustrate the effectiveness of the proposed approach.

The speed, accuracy, and adaptability of human movement depends on the brain performing an inverse kinematics transformation-that is, a transformation from visual to joint angle coordinates-based on learning from experience. In human motion control, it is important to learn a feedback controller for the hand position error in the human inverse kinematics solver. This paper proposes a novel model that uses disturbance noise and the feedback error signal to learn coordinate transformations of the human visual feedback controller. The proposed model redresses drawbacks in current models because it does not rely on complex signal switching, which does not seem neurophysiologically plausible. Numerical simulations show the effectiveness of the model.

Inverse kinematics computation using an artificial neural network that learns the inverse kinematics of a robot arm has been employed by many researchers. However, the inverse kinematics system of typical robot arms with joint limits is a multivalued and discontinuous function. Since it is difficult for a well-known multilayer neural network to approximate such a function, a correct inverse kinematics model cannot be obtained by using a single neural network. In order to overcome the discontinuity of the inverse kinematics function, we proposed a novel modular neural network system that consists of a number of expert neural networks. Each expert approximates the continuous part of the inverse kinematics function. The proposed system uses the forward kinematics model for selection of experts. When the number of the experts increases, the computation time for calculating the inverse kinematics solution also increases without using the parallel computing system. In order to reduce the computation time, we propose a novel expert selection by using the performance prediction networks which directly calculate the performances of the experts.

Inverse kinematics computation using an artificial neural network that learns the inverse kinematics of a robot arm has been employed by many researchers, However, conventional learning methodologies do not pay enough attention the the discontinuity of the inverse kinematics system of typical robot arms with joint limits. The inverse kinematics system of the robot arms is a multi-valued and discontinuous function. Since it is difficult for a well-known multi-layer neural network to approximate such a function, a correct inverse kinematics model for the end-effector's overall position and orientation cannot be obtained by using a single neural network. In order to overcome the discontinuity of the inverse kinematics function, we propose a modular neural network system for the inverse kinematics model learning. We also propose the online learning and control method for trajectory tracking.

In order to grasp an object, we need to solve the inverse kinematics problem, i.e., the coordinate transformation from the visual coordinates to the joint angle vector coordinates of the arm. In human motion control, the learning of the hand position error feedback controller in the inverse kinematics solver is important. Although several models of coordinate transformation learning have been proposed, they suffer from a number of drawbacks. This paper proposes a novel model of the coordinate transformation learning of the human visual feedback controller by using disturbance noise and feedback error signal. The feasibility of the proposed model is illustrated using numerical simulations.

In order to learn an inverse system of a plant, the learning method which inputs the output of the plant to the learner and uses the input of the plant as the desired output signal for the learner has been used in many researches. This learning method for learning control by using a neural network is called "direct inverse modeling" (DIM). Jordan (1995) regarded DIM as a purely off-line learning method and pointed out that DIM is not goal-directed. However, some researches have proposed a different way of using DIM. We deal with online DIM, which is the simultaneous or alternate execution of the plant control and the inverse model learning by DIM. The learning properties of online DIM is completely different from that of off-line DIM. This paper shows that online DIM becomes goal-directed under certain conditions.

Inverse kinematics computation using an artificial neural network that learns the inverse kinematics of a robot arm has been employed by many researchers. However, conventional learning methodologies do not pay enough attention to the discontinuity of the inverse kinematics system of typical robot arms with joint limits. The inverse kinematics system of the robot arms, including a human arm with a wrist joint, is a multivalued and discontinuous function. Since it is difficult for a well-known multilayer neural network to approximate such a function, a correct inverse kinematics model for the end-effector's overall position and orientation cannot be obtained by the conventional methods. In order to overcome the drawbacks of the inverse kinematics solver consisting of a single neural network, we propose a novel modular neural network architecture for the inverse kinematics model learning.

Recognition of its current location is im- portant for a mobile robot to follow a planned path. Landmarks, which are assumed in the environment around the robot, are used to support this recognition. In this paper we propose a method for the robot to select the most suitable landmark among the measur- able landmarks from its current position by evaluating the error covariance update of the position estimation. We use the extended Kalman filter to update the po- sition estimation. We show the update behaviors of two types of landmarks : line type and point type. For the criteria of the evaluation we use the widths of the error ellipses, perpendicular to the direction of the course. Through the simulation of navigating a robot along the center lines of corridors the effectiveness of the proposed method is confirmed. ing landmarks the position estimation is updated in the same way. The behaviors of using two types of landmarks, a line type landmark and a point type one, are examined. The position estimation using a particular landmark is ex- pressed by an error ellipse which is derived from the error covariance update and which shows the directional error distribution of the estimated position error. Though it is favorable if the radius of the error ellipse is small in all di- rections, we think the width of the ellipse along the course is important and propose to use it as the criterion for the selection of the landmark. By the simulation of navigating a robot along the center lines of corridors the effectiveness of navigation based on the proposed landmark selection method is shown.