Search

Search Results

• A METHOD OF CONTROLLING A MOBILE ROBOT USING NATURAL LANGUAGE SEMANTICS

  D.S. Kobzar , V.D. Matveev , Y.D. Lapkin , R.R. Bogdanov , А. S. Izyumov

  2026-04-29

  Abstract ▼

  A large number of different interfaces can be used to control robots, from traditional remotes to augmented reality technologies. However, all such interfaces have a number of limitations, which are particularly acute in service robotics. They are associated with long-term training of a human operator, non-intuitive control for humans, and the need for full human involvement. On the other hand, a new direction has emerged today, related to large language models that are capable of processing natural language and then translating it into robot control commands. There are a number of works demonstrating the possibility of using language models in tasks of planning robot actions. Based on the analysis of existing work, a new method of controlling a mobile robot is proposed, combining the advantages of other methods. The method allows you to plan scenarios for the robot, receiving a natural language mission, the robot's TOP, and information from its sensors. The article also describes the sequence of configuring the system using a large language model to solve this problem. Three variants of instructions for the neural network are presented, which gradually improve the achievability of the generated scenarios. After that, various missions are described, which are set as part of experimental studies - a total of 100 missions were tested, divided into 4 levels of difficulty in equal proportions. The complexity of the missions ranged from describing objects in the robot's field of view to interacting with complex missions involving synonyms of objects and implicitly defined goals. At the end of the work, the results of the evaluation of the algorithm and three variants of the instruction are presented. The conclusion can be considered that the use of language models to assign scenarios to robots is possible, including with a sufficiently high achievement. The model with the most advanced instruction reached 91% of correctly formed scenarios, which suggests the applicability of the developed method for controlling a mobile robot in natural language