The progress of each society depends a great deal on how it treats its most gifted members as well as on whether it cares about adequate development of their potentials. The basis of every progress is creativity. All modern definitions of gift or talent see creativity (besides ability, personality and motivation) as one of its fundamental characteristics. Both theoretical and practical experience show that creativity can be developed and taught if modern educational programs satisfy some fundamental criteria. Among three forms of educational support for gifted students (acceleration, separation and enrichment), we focused on enrichment of educational programs, which is the most accepted form of support. Besides two enrichment program models that are used worldwide (J. Renzulli`s Enriched Triad Model and Bloom`s Taxonomy of Knowledge) we have put special emphasis on creative workshops within the regular curriculum, but also as a part of extracurricular or optional programs. The main aim is to enable children to become aware of themselves and their environmet through some creative activities with emphasis on complete personal development and growth of each individual.

Autonomous service robots assisting in homes and institutions should be able to store and retrieve items in household furniture. This paper presents a neural network-based computer vision method for detection of storage space within storage furniture. The method consists of automatic storage volume detection and annotation within 3D models of furniture, and automatic generation of a large number of depth images of storage furniture with assigned bounding boxes representing the storage space above the furniture shelves. These scenes are used for the training of a neural network. The proposed method enables storage space detection in depth images acquired by a real 3D camera. Depth images with annotations of storage space bounding boxes are also a contribution of this paper and are available for further research. The proposed approach represents a novel research topic, and the results show that it is possible to facilitate a network originally developed for object detection to detect empty or cluttered storage volumes.

Opening doors and drawers will be an important ability for future service robots used in domestic and industrial environments. However, in recent years, the methods for opening doors and drawers have become more diverse and difficult for robots to determine and manipulate. We can divide doors into three distinct handling types: regular handles, hidden handles, and push mechanisms. While extensive research has been done on the detection and handling of regular handles, the other types of handling have not been explored as much. In this paper, we set out to classify the types of cabinet door handling types. To this end, we collect and label a dataset consisting of RGB-D images of cabinets in their natural environment. As part of the dataset, we provide images of humans demonstrating the handling of these doors. We detect the poses of human hands and then train a classifier to determine the type of cabinet door handling. With this research, we hope to provide a starting point for exploring the different types of cabinet door openings in real-world environments.

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