The gradual transition towards the Kazakh language in the Republic of Kazakhstan raises the emergence of applying new technologies for learning the language. Considering the fact that the Kazakh language has dialectal forms, it is important to investigate how these language features would affect the interaction with the synthesized speech of a robot or a computer program. This paper presents a preliminary study exploring the effect of dialectal language on the human-robot interaction in an education-oriented environment. Participants were involved in the interaction with two different robots with pre-programmed language dialectal patterns - South and non-South, to learn new vocabulary. Findings show that there is a low significance in correlation, however, it is suggested that a small sample size led to the obtained results.

Assistive robot solutions are mostly designed as robot helpers with robotic arms and aim to assist disabled and elderly people with carrying out basic activities of daily life such as reaching household objects, i.e. cups, feeding with spoon, opening a drawer/fridge doors, etc., However, commercial assistive robotic arms with joystick control require extensive and tiring hand motor skill training that limits robot's practical usage by patients with disabilities. The main objective of this work is to present the methodology for designing an intelligent human-machine interface for a commercial joystick controlled assistive robotic arm realizing shared autonomy and supervisory control modes. Preliminary results on a RGB-D based object detection and position estimation system development using publicly available YOLOv3 and CenterNet deep learning models implementation of an autonomous object grasping mode by the Kinova Jaco robotic arm are described in detail and experimentally demonstrated.