• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.1
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• pp.165-172
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• 2015

The purpose of this study was to identify the effect of Genibo program robot-based learning(R-Learning) on a pre-schooler's mental state. To achieve above study purpose, the subject of this study was selected 46(teacher 2, five years old pre-schooler 44) from pre-school childrens in Kyongki Y city(R-Learning activity participants group 21: boys 10, girls 11. non-participants 25: boys 13, girls 12). R-Learning program is consist of 5 field about 20 contents using Genibo robot, were applied to the experimental group and the pre-post test was conducted using the EQ assessment tool and observations. The data were analyzed by t-test using the SPSS(ver 18.0) program. The results were as follows: First, the exposure of robots to pre-schoolers in practical situation has shown positive influence to the children's emotional well-being. Positive improvements were observed in the four sub categories of the EQ assessment after exposure. Second, the Genibo used for this study, is a biomimetic AI based robot mimicking the behavior of a pet dog. This is related more or less to the specifications of a pre-school education where animals are used as a 'friendly medium' to facilitate the learning process. Third, the robot exposure gave benefit to all the ones in the sample, regardless of sex. Furthermore, It is suggested that promising potential for robots to be utilized as a new educational media plus facilitator, R-Learning is related more or less to the specifications of a pre-school education where animals are used as a 'friendly medium' to facilitate the learning process, and when applying them for education, stereotyping the likes of sex is overrated - instead, the focus should be more on the pre-schoolers' / childrens' individual traits, learning curve differences and alike.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.264-266
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• 2009

A skill is defined as the special ability to do something well, especially as acquired by learning and practice. To learn a skill, a Bayesian network model for representing the skill is first learned. We will regard the Bayesian network for a skill as an affordance. We propose a soft Behavior Motivation(BM) switch as a method for ordering affordances to accomplish a task. Then, a skill is constructed as a combination of an affordance and a soft BM switch. To demonstrate the validity of our proposed method, some experiments were performed with GENIBO(Pet robot) performing a task using skills of Search-a-target-object, Approach-a-target-object, Push-up-in front of -a-target-object.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.4
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• pp.7-16
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• 2009

A robot must be able to generate various skills to achieve given tasks intelligently and reasonably. The robot must first learn affordances to generate the skills. An affordance is defined as qualities of objects or environments that induce actions. Affordances can be usefully used to generate skills. Most tasks require sequential and goal-oriented behaviors. However, it is usually difficult to accomplish such tasks with affordances alone. To accomplish such tasks, a skill is constructed with an affordance and a soft behavioral motivation switch for reflecting goal-oriented elements. A skill calculates a behavioral motivation as a combination of both presently perceived information and goal-oriented elements. Here, a behavioral motivation is the internal condition that activates a goal-oriented behavior. In addition, a robot must be able to execute sequential behaviors. We construct skill networks by using generated skills that make action selection feasible to accomplish a task. A robot can select sequential and a goal-oriented behaviors using the skill network. For this, we will first propose a method for modeling and learning Bayesian networks that are used to generate affordances. To select sequential and goal-oriented behaviors, we construct skills using affordances and soft behavioral motivation switches. We also propose a method to generate the skill networks using the skills to execute given tasks. Finally, we will propose action-selection-mechanism to select sequential and goal-oriented behaviors using the skill network. To demonstrate the validity of our proposed methods, "Searching-for-a-target-object", "Approaching-a-target-object", "Sniffing-a-target-object", and "Kicking-a-target-object" affordances have been learned with GENIBO (pet robot) based on the human teaching method. Some experiments have also been performed with GENIBO using the skills and the skill networks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.9
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• pp.1964-1972
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• 2009

This paper proposed a distributed web environment for developing robot services using a 4-legged robot and MSRS. The proposed development environment of the robot services enabled a high-level complicated robot application to operate in a distributed web environment by implementing robot functions as services which can be run on network nodes using Microsoft MSRS. The environment has been also implemented using a 4-legged robot, Genibo as a robot platform. In experiment, we showed the developed robot services are enable to be executed and configured dynamically and support a remote monitoring in a distributed web environment. Finally the feasibility and the effectiveness of the proposed environment has been verified by creating a robot application using the developed robot services in Visual Program Language which helps student to create a complicated robot application system easily.