Noriaki Mitsunaga

The scientifical experiment teaching materials handled at school have a lot of definition reactions and have few quantity experiments. But it is important to raise the thinking power and be considered from an obtained experimental result. In this study, the performance of a simple handmade colorimeter which can be easily used as the teaching materials was estimated by determination of content of starch in powder sugar. Our colorimeter was composed of LED and photoresistor. Three devices were constructed and used for the quantitative analysis. The obtained values of the content of starch were compared each other and no significant error was observed. The amount of starch in commercial powder sugar was almost the same as the value determined by UV-visible spectrophotometer. Therefore, the simple handmade colorimeter was useful for the class of the science and the home economics at school.

タブレット端末が普及するにともない，パソコンではなくタブレット上でのプログラミング環境も増えつつある．最近ではマイクロコントローラあるいはマイクロコンピュータを省略してマイコンとよばれる1チップから数チップで計算機を構築できるコンピュータが，家電，車といった製品の中だけでなく，趣味の電子工作や学校でのプログラミングの学習にも使われている．しかし，そのプログラミングに使えるタブレット端末上のビジュアルプログラミング環境はみあたらない．もし，そのような環境があればマイコンや電子工作に親しむ層が増えると期待される．ところで，マイコンにインタプリタを載せればプログラミング環境にコンパイラは不要であり，デバッグ情報をインタプリタから提供すれば，タブレット端末のような資源の限られたコンピュータでもプログラムの動作状態やマイコンの入出力といった情報を表示するプログラミング環境を実現しやすい．そこで，マイコンにインタプリタを搭載し，そのプログラムを開発するタブレット端末用ビジュアルプログラミング環境aiBlocksを実現したので，初めてプログラミングと電子工作に取り組んだ初心者によるaiBlocksの評価とともに報告する．In recent years, tablet PCs are becoming popular and number of visual programming environments on them are increasing. Meanwhile, micro controllers, small computers that consist of single or a few ICs, are becoming popular among hobyists and programming education. We beleive that a visual programming environment to build a micro controller program on a tablet PC makes more pepole to familar with micro controllers and electronical circuits. However, no such visual programming environment on tablet PC has been published as far as we know. Then, we have developed such an environment named "aiBlocks" on Android OS. In this paper, we report how we developed aiBlocks and how beginners used it.

学校での理科教育では棒温度計や可動コイル形電圧計・電流計といった計器が広く用いられている．一方で表示自由度の高い液晶等の表示パネルの価格が低下し利用しやすくなったことから，そういった計器の表示の置き換えが学校外では進んでいる．現在，タブレット端末が学校へも普及しつつある．タブレット端末の表示自由度は従来の計器よりも高い．本研究では，その特長を活かし，数値，アナログメータ，棒温度計を模擬，グラフ（オシロスコープ）形式による表示を実現するiTesterを開発したので報告する．本報告では，まずiTesterの開発経緯を述べる．そして，実際の小中学校での利用結果から，表示器としてのタブレット端末の利用が1）視認性の面で従来の計器よりも学校教育現場に適していること，2）従来より精度の高い測定の可能性と，3）教育の質的な変化をもたらす可能性を報告し，支援学校での利用結果から，4）計器のユニバーサルデザインの実現について報告する．

In this paper, we report the importance of the reactive behaviors of humanoid robots against human actions for smooth communication. We hypothesize that the reactive behaviors of robots play an important role in achieving human-like communication between humans and robots since the latter need to be recognized by the former as communication partners. To evaluate this hypothesis, we conducted psychological experiments in which we presented subjects with four types of reactive behaviors resulting from pushing a wheeled inverted-pendulum-type humanoid robot. From the experiment, we found that subject's impressions to the robot regarding extroversion and neuroticism changed by the robot's reactive behaviors. We also discuss the reasons for such changes in impressions by comparing the robot's and human reactive behavior.

In the near future, robots are expected to actively participate in our daily lives. Once this time arrives, they will need to be socially accepted by people in various communities. However, the issues remain unknown that must be solved to make robots socially accepted. We have conducted a long-term experiment that a communication robot interacts people daily in an office. From the questionaire answers for the experiment, we have found that three fundamental issues, offering familiarity, reading the situation, and playing a social role, are required for a robot that is socially accepted. In this paper, we propose a hypothesis that three fundamental issues must be fulfilled by a socially accepted robot. Then, we tested our hypothesis through a six-week experiment in an office. We present the details of the experimental results and discuss what is the most important issue among them for a socially accepted robot.

Humans always sway their body when they are standing. Since the swaying is natural for human, they are not conscious of the swaying. However, today, almost all robots are designed to reduce the swaying to ensure stabilities. If communication robots can control the swaying appropriately, it might help humans to anthropomorphize the robot. In this paper, we evaluate how the swaying of a humanoid robot affects human swaying and their impressions. We measured human and robot's neck movement and asked them to answer a questionnaire after the experiments. We discovered that human swaying and impressions were affected by the robot's swaying. Human swaying is always observed whenever communications are performed. In order to apply the swaying to humanoid robots, we only use wheel control. Additionally, we can make the robot's swaying and do other actions on their upper bodies. So the result of this paper will explain how to make robots' swaying.

Human beings subconsciously adapt their behaviors to a communication partner in order to make interactions run smoothly. In human-robot interactions, not only the human but also the robot is expected to adapt to its partner. Thus, to facilitate human-robot interactions, a robot should be able to read subconscious comfort and discomfort signals from humans and adjust its behavior accordingly, just like a human would. However, most previous, research works expected the human to consciously give feedback, which might interfere with the aim of interaction. We propose an adaptation mechanism based on reinforcement learning that reads subconscious body signals from a human partner, and uses this information to adjust interaction distances, gaze meeting, and motion speed and timing in human-robot interactions. The mechanism uses gazing at the robot's face and human movement distance as subconscious body signals that indicate a human's comfort and discomfort. A pilot study with a humanoid robot that has ten interaction behaviors has been conducted. The study result of 12 subjects suggests that the proposed mechanism enables autonomous adaptation to individual preferences. Also, detailed discussion and conclusions are presented.

In this paper we propose an intuitive page-turning and browsing interface of e-books on a flexible e-paper based on user studies. Our user studies showed various types of page-turning actions such as flipping, grasping, and sliding by different situations or users. We categorized these actions into three categories: turn, flip through, and leaf through the page(s). Based on this categorized model, we have developed a conceptual design and prototype of an interface for an e-book reader, which enables intuitive page-turning interactions using a simple architecture in both hardware and software design. The prototype has a flexible plastic sheet with bend sensors, which is attached to a small LCD monitor to physically unite the visual display with a tangible control interface based on the natural page-turning actions as used in reading a real book. The prototype handles all three page-turning actions observed in the user studies by interpreting the bend degree of the sheet.Copyright 2008 ACM.

We believe that humanoids will take an active part in our daily lives in the near future as important media, since a human-size humanoid enables people to more easily accept a sense of humanlike expressions or emotions, wishes, and so forth in addition to capabilities of other media, such as the ability to collect and provide information from the Internet and via ubiquitous sensors connected through a network. Once this time arrives, humanoids 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.” Robovie-IV features interaction abilities such as the ability to chat vocally, which are intended to be “social interactions.” Using Robovie-IV we have conducted an experiment in which it interacts with people in an everyday office environment. This paper discusses the design requirements of Robovie-IV and introduces an overview of its hardware and software architectures. Then, the experimental results, discussions, and conclusions are presented.

When humans interact in a social context, there are many factors apart from the actual communication that need to be considered. Previous studies in behavioral sciences have shown that there is a need for a certain amount of personal space and that different people tend to meet the gaze of others to different extents. For humans, this is mostly subconscious, but when two persons interact, there is an automatic adjustment of these factors to avoid discomfort. In this paper we propose an adaptation mechanism for robot behaviors to make human-robot interactions run more smoothly. We propose such a mechanism based on policy gradient reinforcement learning, that reads minute body signals from a human partner, and uses this information to adjust interaction distances, gaze meeting, and motion speed and timing in human-robot interaction. We show that this enables autonomous adaptation to individual preferences by the experiment with twelve subjects.

Most current mobile robots are designed to determine their actions according to their positions. Before making a decision, they need to localize themselves. Thus, their observation strategies are mainly for self-localization. However, observation strategies should not only be for self-localization but also for decision making. We propose an observation strategy that enables a mobile robot to make a decision. It enables a robot equipped with a limited viewing angle camera to make decisions without self-localization. A robot can make a decision based on a decision tree and on prediction trees of observations constructed from its experiences. The trees are constructed based on an information criterion for the action decision, not for self-localization or state estimation. The experimental results with a four legged robot are shown and discussed.

In this paper we propose an adaptation mechanism for robot behaviors to make robot-human interactions run more smoothly. We propose such a mechanism based on reinforcement learning, which reads minute body signals from a human partner, and uses this information to adjust interaction distances, gaze meeting, and motion speed and timing in human-robot interaction. We show that this enables autonomous adaptation to individual preferences by an experiment with twelve subjects.

This paper proposes a subjective map representation that enables a robot in a multi-agent system to make decisions in a dynamic, hostile environment. A typical situation can be found in the Sony four-legged robot league of the RoboCup competition. The subjective map is a map of the environment that each agent maintains regardless of the objective consistency of the representation among the agents. Due to the map's subjectivity, it is not affected by incorrect information acquired by other agents. The method is compared with conventional methods with or without information sharing.

Visual attention is one of the most important issues for a vision guided mobile robot. Methods have been proposed for visual attention control based on information criterion [3] [9] . However, the robot had to stop walking for observation and decision. This paper presents a method which enables observation and decision more efficiently and adaptively while it is walking. The method uses the expected information gain from future observations for attention control and action decision. It also proposes an image compensation method to handle the image changes due to the robot motion. Both are used to estimate observation probabilities from the observation while it is walking and then action probabilities are estimated from a decision tree based on the information criterion. The method is applied to a four legged robot. Discussions on the visual attention control in the method and the future issues are given.

This paper proposes a subjective map representation that enables a multiagent system to make decisions in a dynamic, hostile environment. A typical situation can be found in the Sony four-legged robot league of the RoboCup competition [1]. The subjective map is a map of the environment that each agent maintains regardless of the objective consistency of the representation among the agents. Owing to the map's subjectivity, it is not affected by incorrect information belonging to other agents. For example, it is not affected by non-negligible errors caused by dynamic changes in the environment, such as falling down or being picked up and brought to other places by the referee. A potential field is defined on the subjective map in terms of subtasks, such as approaching and shooting the ball, and the field is dynamically updated so that the robot can decide what to do next. This methods is compared with conventional methods that involve sharing or not sharing information.

Visual attention is one of the most important issues for a mobile robot to accomplish a given task in complicated environments since the vision sensors bring a huge amount of data. This paper proposes a method of sensor space segmentation for visual attention control that enables efficient observation taking the time needed for observation into account. The efficiency is considered from a viewpoint of not geometrical reconstruction but unique action selection based on information criterion regardless of localization uncertainty. The method is applied to a four legged robot that tries to shoot a ball into the goal. To build a decision tree, a training set is given by the designer, and a kind of off-line learning is performed on the given data set. Discussion on the visual attention control in the method is given and the future issues are shown.

Visual attention is one of the most important issues for a vision guided mobile robot. Methods have been proposed for visual attention control based on information criterion[3, 4]. However, the robot had to stop walking for observation and decision. This paper presents a method which enables observation and decision more efficiently and adaptively while it is walking. The method uses the expected information gain from future observations for attention control and action decision. It also proposes an image compensation method to handle the image changes due to the robot motion. Both are used to estimate observation probabilities from the observation while it is walking and then action probabilities are estimated from a decision tree based on the information criterion. The Method is applied to a four legged robot. Discussions on the visual attention control in the method and the future issues are given.

This paper proposes a method for constructing a decision tree and prediction ones of the landmarks that enable a robot with a limited visual angle to make decisions without self-localization in the environment. Since global positioning from the 3-D reconstruction of landmarks is generally time-consuming and prone to errors, the robot makes decisions depending on the appearance of landmarks. By using the decision and the prediction trees based on information criterion, the robot can achieve the task efficiently.

Visual attention is one of the most important issues for a vision guided mobile robot not simply because visual information bring a huge amount of data but also because the visual field is limited, therefore gaze control is necessary. This paper proposes a method of sensor space segmentation for visual attention control that enables mobile robots to realize efficient observation. The efficiency is considered from a viewpoint of not geometrical reconstruction but unique action selection based on information criterion regardless of localization uncertainty. The method builds a decision tree based on the information criterion while taking the time needed for observation into account, and attention control is done by following the tree. The tree is rebuilt by introducing contextual information for more efficient attention control. The method is applied to a four legged robot that tries to shoot a ball into the goal. Discussion on the visual attention control in the method is given and the future issues are shown.

Self localization seems necessary for mobile robot navigation. The conventional method such as geometric reconstruction from landmark observations is generally time-consuming and prone to errors. This paper proposes a method which constructs a decision tree and prediction trees of the landmark appearance that enable a mobile robot with ct limited visual angle to observe efficiently and make decisions without global positioning in the environment. By constructing these trees based on information criterion, the robot can accomplish the given task efficiently. The validity of the method is shown with a four legged robot.