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Abstract X-ray topography is a powerful method for analyzing crystal defects and strain in crystalline materials non-destructively. However, conventional X-ray topography uses simple X-ray diffraction images, which means depth information on defects and dislocations cannot be obtained. We have therefor developed a novel three-dimensional micro-X-ray topography technique (3D μ-XRT) that combines Bragg-case section topography with focused sheet-shaped X-rays. The depth resolution of the 3D μ-XRT depends mainly on the focused X-ray beam size and enables non-destructive observation of internal defects and dislocations with an accuracy on the order of 1 μm. The demonstrative observation of SiC power device chips showed that stacking faults, threading screw, threading edge, and basal plane dislocations were clearly visualized three-dimensionally with a depth accuracy of 1.3 μm. 3D μ-XRT is a promising new approach for highly sensitive and non-destructive analysis of material crystallinity in a three-dimensional manner.

Abstract Human-like environment recognition by musculoskeletal humanoids is important for task realization in real complex environments and for use as dummies for test subjects. Humans integrate various sensory information to perceive their surroundings, and hearing is particularly useful for recognizing objects out of view or out of touch. In this research, we aim to realize human-like auditory environmental recognition and task realization for musculoskeletal humanoids by equipping them with a human-like auditory processing system. Humans realize sound-based environmental recognition by estimating directions of the sound sources and detecting environmental sounds based on changes in the time and frequency domain of incoming sounds and the integration of auditory information in the central nervous system. We propose a human mimetic auditory information processing system, which consists of three components: the human mimetic binaural ear unit, which mimics human ear structure and characteristics, the sound source direction estimation system, and the environmental sound detection system, which mimics processing in the central nervous system. We apply it to Musashi, a human mimetic musculoskeletal humanoid, and have it perform tasks that require sound information outside of view in real noisy environments to confirm the usefulness of the proposed methods.

Social robots have grown increasingly integrated into our daily lives in recent years. Robots can be good social agents who engage with people, such as assistants and counselors, and good partners and companions with whom people can form good relationships. Furthermore, unlike devices such as smart speakers or virtual agents on a screen, robots have physicality, which allows them to observe the actual environment using sensors and respond behaviorally with full-body motions. In order to engage people in dialogue and create good relationships with robots as close partners, real-time interaction is important. In this article, we present a dialogue system platform developed with the aim of providing robots with social skills. We also built a system architecture for the robot to respond with speech and gestures within the dialogue system platform, which attempts to enable natural engagement with the robot and takes advantage of its physicality. In addition, we think the process called “co-creation” is important to build a good human–robot interaction system. Engineers must bridge the gap between users and robots in order for them to interact more effectively and naturally, not only by building systems unilaterally but also from a range of views based on the opinions of real users. We reported two experiments using the developed dialogue interaction system with a robot. One is an experiment with elderly people as the initial phase in this co-creation process. The second experiment was conducted with a wide range of ages, from children to adults. Through these experiments, we can obtain a lot of useful insights for improving the system. We think that repeating this co-creation process is a useful approach toward our goal that humans and robots can communicate in a natural way as good partners such as family and friends.

Abstract This paper proposes a locomotion approach of leg-wheel robot utilizing passive wheel attached to the foot of bipedal robot. The key feature of this approach is bipedal mobility without swing leg. This mobility contributes the stability based on expansion of support polygon during locomotion, the robustness for obstacles and stopping to prevent fall, and the adaptability by prevention of body swing sideways. To achieve these, we propose the stability margin maximization to optimize center of gravity projection for support polygon and the fall prevention functions for real environment that is a difficult situation to prevent unexpected fall by the only planning. Finally, we apply the proposed methods to leg-wheel phases through locomotion and verify the contribution by experiments using real bipedal robot.

Dynamic manipulation of flexible objects such as fabric, which is difficult to modelize, is one of the major challenges in robotics. With the development of deep learning, we are beginning to see results in simulations and some actual robots, but there are still many problems that have not yet been tackled. Humans can move their arms at high speed using their flexible bodies skillfully, and even when the material to be manipulated changes, they can manipulate the material after moving it several times and understanding its characteristics. Therefore, in this research, we focus on the following two points: (1) body control using a variable stiffness mechanism for more dynamic manipulation, and (2) response to changes in the material of the manipulated object using parametric bias. By incorporating these two approaches into a deep predictive model, we show through simulation and actual robot experiments that Musashi-W, a musculoskeletal humanoid with a variable stiffness mechanism, can dynamically manipulate cloth while detecting changes in the physical properties of the manipulated object.

This paper illustrates the development of active self-melting bolt aiming at self-healing tendon-driven robot with high-load endurance. Recent pioneering research has explored several methods for applying self-healing principles for robotics. For example, exploiting reversible crosslinking bonds are a major approach. They enable conventional robots to gain selfrepairing capability from micro damages such as stubs and so on. However, providing a healing module with large load endurance and autonomous healing capability is yet a challenging task. Then, we categorized design challenges to construct such a module. In addition, we introduced some alternatives to overcome the problems. First, we formulated fracture and healing energy ratio to select proper material for active healing structure. The measure leads the authors to the statement that a low melting point alloy is a powerful alternative. Next, the authors insist that bolted joint structure is a candidate for self-healing module with high-load endurance. Finally, we validated the feasibility of the module with tensile tests using motor-driven tendon unit that has been developed by our group. The result showed that proposed structure had healing capability in spite of its low healing ratio. In summary, the paper presents design, manufacturing method and validation for active self-melting bolt.

This paper proposes the online learning controller for PID gain tuning to regulate the speed of robot on parallel two-wheeled electric scooter. We define this speed regulation as “speed governing behavior”. The proposed system based on Iterative Feedback Tuning (IFT) contributes the online continuity during learning control not to interrupt and reset/restart the controller. PID gain update during control has the problem that the integral gain change affects the divergence of control input. To this problem, we propose the digital integrator which modifies the previous integrated value of control error. This proposed integrator solves the impulsive control input without any anti-windup or reference shaping. In conclusion, this paper shows the experimental results based on the online learning control and demonstrates the speed governing behavior by life-sized humanoid robot. We finally aim the driving of parallel two-wheeled electric scooter by speed control of humanoid robot.

This paper describes an autonomous robot's method of dressing a subject in clothing. Our target task is to dress a person in the sitting pose. We especially focus on the action whereby a robot automatically pulls a pair of trousers up the subject's legs, an action frequently needed in dressing assistance. To avoid injuring the subject's legs, the robot should be able to recognize the state of the manipulated clothing. Therefore, while handling the clothing, the robot is supplied with both visual and tactile sensory information. A dressing failure is detected by the visual sensing of the behaviour of optical flows extracted from the clothing's movements. The effectiveness of the proposed approach is implemented and validated in a life-sized humanoid robot.

In order to realize more natural and various motions like humans, humanlike musculoskeletal tendon-driven humanoids have been studied. Especially, it is very challenging to design musculoskeletal body structure which consists of complicated bones, redundant powerful and flexible muscles, and large number of distributed sensors. In addition, it is very challenging to reveal humanlike intelligence to manage these complicated musculoskeletal body structure. This paper sums up life-sized musculoskeletal humanoids Kenta, Kotaro, Kenzoh and Kenshiro which we have developed so far, and describes key technologies to develop and control these robots.