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People with disabilities, such as patients with motor paralysis conditions, lack independence and cannot move most parts of their bodies except for their eyes. Supportive robot technology is highly beneficial in supporting these types of patients. We propose a gaze-informed location-based (or gaze-based) object segmentation, which is a core module of successful patient-robot interaction in an object-search task (i.e., a situation when a robot has to search for and deliver a target object to the patient). We have introduced the concepts of gaze tracing (GT) and gaze blinking (GB), which are integrated into our proposed object segmentation technique, to yield the benefit of an accurate visual segmentation of unknown objects in a complex scene. Gaze tracing information can be used as a clue as to where the target object is located in a scene. Then, gaze blinking can be used to confirm the position of the target object. The effectiveness of our proposed method has been demonstrated using a humanoid robot in experiments with different types of highly cluttered scenes. Based on the limited gaze guidance from the user, we achieved an 85% F-score of unknown object segmentation in an unknown environment. [ABSTRACT FROM AUTHOR]

This paper presents a non-contact manipulation method using rotational flow generated by high speed motion of a glass needle. In order to generate an applicable motion that can precisely control the speed and position of a target object around an end effector, a manipulator actuated by three piezoelectric actuators is proposed. A compact parallel link produces precise three-dimensional motions. To reach the required end-effector frequency and amplitude, which is necessary for generating the rotational flow that can make rotational movement of microbeads having patterned data, the end effector motion is generated by controlling the three piezoelectric actuators at high speed. In this paper, we focus on rotational flow created by two-dimensional circular motion of the end effector mounted on the parallel link. By changing the amplitude and frequency of the circular motion, the generated swirl flow is analyzed in 3D space using 9.6-μm microbeads. We analyzed the velocity toward to the root of the end effector and angular velocity around the end effector by formulating models. From the analyzed data, we verified that the rotational flow has potential to manipulate micro-objects precisely in 3D space. [ABSTRACT FROM AUTHOR]

Keyframe selection is the process of finding a set of representative frames from an image sequence. We aim to achieve an automatic keyframe selection. The main problem is that the accuracy of keyframe selection is highly subjective to each particular user. To deal with this problem, we propose a VIEMO keyframe selection framework based on visual and excitement features that consists of two integrated modules, namely; event segmentation and keyframe selection. Firstly, scene change detection algorithm was applied for an event segmentation. Later, visual features which are contrast, color variance, sharpness, noise and saliency along with excitement features from a biosensor are used to filter keyframe that closely matches with user selection keyframe. Two different fusion scheme which are flat and hierarchical fusion were also investigated. To evaluate the quality of keyframe from the proposed method, we present an evaluation techniques which grades the quality of the keyframe automatically. Even when the keyframe does not exactly match with the keyframe selected by the user, the degree of acceptance calculated from visual similarity is provided. Experimental results showed that keyframe selection using only visual features yielded an acceptance rate of $$74.16 \,\%$$ . Our proposed method achieves a higher acceptance rate of $$83.71\,\%$$ . Moreover, the acceptance rate was improved by the average of $$9.55\,\%$$ in all participants. Therefore, our framework provides a potential solution to this subjective issue for keyframe selection in lifelog image sequences selection. [ABSTRACT FROM AUTHOR]

As part of the face recognition task in a robust security system, we propose a novel approach for the illumination recovery of faces with cast shadows and specularities. Given a single 2D face image, we relight the face object by extracting the nine spherical harmonic bases and the face spherical illumination coefficients by using the face spherical spaces properties. First, an illumination training database is generated by computing the properties of the spherical spaces out of face albedo and normal values estimated from 2D training images. The training database is then discriminately divided into two directions in terms of the illumination quality and light direction of each image. Based on the generated multi-level illumination discriminative training space, we analyze the target face pixels and compare them with the appropriate training subspace using pre-generated tiles. When designing the framework, practical real-time processing speed and small image size were considered. In contrast to other approaches, our technique requires neither 3D face models nor restricted illumination conditions for the training process. Furthermore, the proposed approach uses one single face image to estimate the face albedo and face spherical spaces. In this work, we also provide the results of a series of experiments performed on publicly available databases to show the significant improvements in the face recognition rates. [ABSTRACT FROM AUTHOR]

In this study, we focus on what perspectives people use to perceive a humanoid, and we explore the basic dimensions of a humanoid evaluation by ordinary people. We also develop the psychological scale called PERNOD (PERception to humaNOiD), a humanoid-oriented scale that is based on fundamental dimensions. Several studies have investigated the psychological evaluation of robots interaction with humans; however, almost studies have methodological limitations because few scales for the evaluation of psychological impressions of humanoids have been developed thus far. This study uses a humanoid of HRP-2 and explores what are the perspectives to evaluate the robot and investigate importance to develop humanoid-oriented scale. With free descriptions, 157 respondents provided impressions of a humanoid robot, and psychologists categorized the qualitative data into several groups according to psychological meanings of the impressions. Thereafter, the descriptions were organized in the form of a Likert scale in order to develop PERNOD, and 380 additional respondents evaluated a humanoid robot quantitatively using PERNOD. The result of factor analysis indicated that PERNOD comprises five basic dimensions for perceiving humanoid robots: Familiarity, Utility, Motion, Controllability, and Toughness. Some similarities are found in dimensions of evaluation for both humanoids and humans, and original dimensions are found in dimensions for humanoids. The reliability of each sub-scale of PERNOD is statistically high; further, the usability of this scale is discussed. [ABSTRACT FROM AUTHOR]

We present the novel framework of knowledge construction (ICC: Independent Co-occurring based Construction) based on co-occurrence relations of objects. We compare its characteristics with that of general approach (DCC: Dependent Co-occurring based Construction) in various construction aspects: variations of trained probability values, percentage differences (probability value and priority ranking order), and reconstruction time. The similarity of their data content and faster reconstruction time of ICC suggest that ICC is more suitable for applications of service robot. Instead of using visual feature, we employed annotated data, such as word-tagging images, as the training set to increase the accuracy of correspondence between related keywords and images. The task of object search in unknown environment is selected to evaluate the applicability of using constructed knowledge (OCR: Object Co-occurrence Relations). We explore the search behaviors, provided by OCR-based search (indirect search) and greedy search (direct search), in simulation experiments with five different starting robot positions. Their search behaviors are also compared from the aspects of consumed computational time, travel distance, and number of visited locations. The certainty of success of OCR-based search assures us of its benefit. Moreover, the object search experiment in unknown human environment is conducted by a mobile robot, equipped with a stereo camera, to show the possibility of using OCR in the search in real world. [ABSTRACT FROM AUTHOR]

We present a system architecture for domestic robots that allows them to learn object categories after one sample object was initially learned. We explore the situation in which a human teaches a robot a novel object, and the robot enhances such learning by using a large amount of image data from the Internet. The main goal of this research is to provide a robot with capabilities to enhance its learning while minimizing time and effort required for a human to train a robot. Our active learning approach consists of learning the object name using speech interface, and creating a visual object model by using a depth-based attention model adapted to the robot's personal space. Given the object's name (keyword), a large amount of object-related images from two main image sources (Google Images and the LabelMe website) are collected. We deal with the problem of separating good training samples from noisy images by performing two steps: (1) Similar image selection using a Simile Selector Classifier, and (2) non-real image filtering by implementing a variant of Gaussian Discriminant Analysis. After web image selection, object category classifiers are then trained and tested using different objects of the same category. Our experiments demonstrate the effectiveness of our robot learning approach. [ABSTRACT FROM AUTHOR]

The paper presents a robot system design with highly reusable components for a component-based robot system for manipulation tasks. The robot system is designed based on the analysis of manipulation tasks using a unified modeling language use case diagram. For a service robot with locomotion and manipulation mechanisms, reusability of robot system components is improved by adopting the proposed design. Our structure consists of scenario, task, robot information management server, data analyzer, sensor hardware controller, skill, and motion hardware controller on a component-based robot system. Based on the proposed robot system, we implemented a component-based robot system and subsequently realized a grasping motion by a service robot. [ABSTRACT FROM AUTHOR]

In this paper, we propose two gait strategies for a limb mechanism robot, called ASTERISK, to move on narrow spaces. Specifically, we describe two types of locomotion, namely, vertical-body climbing gait and horizontal-body climbing gait, that the robot uses to climb on two parallel walls. The proposed strategies are verified through both simulations and experiments on an actual robot. Moreover, to reduce power consumption during locomotion, we employ a power efficiency model based on the pose of the robot and its limbs. The simulation and experimental results confirm the effectiveness of proposed gait strategies. [ABSTRACT FROM AUTHOR]