Kyoto University (京都大学) / 0048 / 新制・課程博士 / 博士(工学) / 甲第15664号 / 工博第3322号 / 新制||工||1501(附属図書館) / 28201 / 京都大学大学院工学研究科機械理工学専攻 / (主査)教授 椹木 哲夫, 教授 松原 厚, 教授 西脇 眞二 / 学位規則第4条第1項該当

Stroke is an age-related illness with significant individual and societal impacts. The long term impacts associated with many strokes can be mitigated with timely rehabilitation. Therapy robots have been introduced to these programs in an effort to reduce the economic burden to society and to improve the level of care provided to stroke survivors. The purpose of this thesis is to develop a teaching mode for an upper limb therapy robot. The system will allow physiotherapists to interact with the therapy robot without the need for any specialized industrial training. At the same, the system will reduce the data associated with patient movements to reduce requirements for robot safety and motion systems. The proposed system was successfully confirmed using a laboratory scale industrial robot and a standalone motion control system consisting of commercially available AC servo motors and a motion controller with both generated and recorded paths.

Augmented Reality (AR) is an emerging technology that utilizes computer vision methods to overlay virtual objects onto the real world scene so as to make them appear to co-exist with the real objects. Its main objective is to enhance the user’s interaction with the real world by providing the right information needed to perform a certain task. Applications of this technology in manufacturing include maintenance, assembly and telerobotics. In this paper, we explore the potential of teaching a robot to perform an arc welding task in an AR environment. We present the motivation, features of a system using the popular ARToolkit package, and a discussion on the issues and implications of our research. / Singapore-MIT Alliance (SMA)

Although many researchers have pointed out that educational robots can motivate student learning, learning motivation inevitably declines over time. The sustainability of learning motivation is closely related to instructional strategies. In other words, appropriate instructional strategies are still essential to sustain a learner's learning motivation in robot-assisted instructions. The ARCS model provides systematic guidelines for enhancing and sustaining learning motivation; however, it provides very limited instructional practices in the model. Recent development in educational robot grows rapidly. A humanoid robot, which has a tangible and attractive body and limbs, is able to perform movements and gestures and to interact with its users. This study aims to develop instructional strategies and activities based on the ARCS model and the specific features of the robot teaching assistant (RTA) to enhance and sustain motivation in learning English reading skills. A quasi experiment and a survey were conducted to evaluate the effects of the designed strategies and activities. The results showed that the design of the RTA-based activities following the ARCS model for learning English reading skills was positively and significantly contributed to students¡¦ learning motivation, learning performance and continuance intention.

This thesis was motivated by a study of how robots can be taught by humans, with an emphasis on allowing persons without programming skills to teach robots. The focus of this thesis was to investigate what criteria could or should be used by a robot to evaluate whether a human teacher is (or potentially could be) a good teacher in robot learning by demonstration. In effect, choosing the teacher that can maximize the benefit to the robot using learning by imitation/demonstration. The study approached this topic by taking a technology snapshot in time to see if a representative example of research laboratory robot technology is capable of assessing teaching quality. With this snapshot, this study evaluated how humans observe teaching quality to attempt to establish measurement metrics that can be transferred as rules or algorithms that are beneficial from a robot's point of view. To evaluate teaching quality, the study looked at the teacher-student relationship from a human-human interaction perspective. Two factors were considered important in defining a good teacher: engagement and immediacy. The study gathered more literature reviews relating to further detailed elements of engagement and immediacy. The study also tried to link physical effort as a possible metric that could be used to measure the level of engagement of the teachers. An investigatory experiment was conducted to evaluate which modality the participants prefer to employ in teaching a robot if the robot can be taught using voice, gesture demonstration, or physical manipulation. The findings from this experiment suggested that the participants appeared to have no preference in terms of human effort for completing the task. However, there was a significant difference in human enjoyment preferences of input modality and a marginal difference in the robot's perceived ability to imitate. A main experiment was conducted to study the detailed elements that might be used by a robot in identifying a 'good' teacher. The main experiment was conducted in two subexperiments. The first part recorded the teacher's activities and the second part analysed how humans evaluate the perception of engagement when assessing another human teaching a robot. The results from the main experiment suggested that in human teaching of a robot (human-robot interaction), humans (the evaluators) also look for some immediacy cues that happen in human-human interaction for evaluating the engagement.