The topic of this final qualification work was chosen due to the importance of developing robotic systems designed to assist people with disabilities. Advances in robotics and automation technologies have opened up new prospects for creating devices that can significantly improve the quality of life for these people. In this context, designing a robotic hand with a control system adapted to the needs of people with disabilities is a major scientific and practical challenge. This work addresses the problem of developing and manufacturing a four-degree-of-freedom robotic hand suitable for practical manipulation. Addressing this issue requires a comprehensive approach, encompassing the design of the hand's mechanical structure, the development of its control system, and its integration with a technical vision system and software based on the Robot Operating System (ROS).

Exploring the predictive capabilities of language models in material science is an ongoing interest. This study investigates the application of language model embeddings to enhance material property prediction in materials science. By evaluating various contextual embedding methods and pre-trained models, including Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformers (GPT), we demonstrate that domain-specific models, particularly MatBERT significantly outperform general-purpose models in extracting implicit knowledge from compound names and material properties. Our findings reveal that information-dense embeddings from the third layer of MatBERT, combined with a context-averaging approach, offer the most effective method for capturing material-property relationships from the scientific literature. We also identify a crucial "tokenizer effect," highlighting the importance of specialized text processing techniques that preserve complete compound names while maintaining consistent token counts. These insights underscore the value of domain-specific training and tokenization in materials science applications and offer a promising pathway for accelerating the discovery and development of new materials through AI-driven approaches.