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Abstract <p>Advances in language models open up opportunities for the creation of intelligent robot-control systems capable of interpreting general, often vaguely formulated, human target commands and shaping robot behavior based on them under given conditions. The paper proposes a system for the reasoned control of the robot’s behavior, which is the result of logical processing of goal-setting instructions using external information from the robot’s environment, taking into account feedback from the operator. Experiments were conducted to evaluate the success rate of a system of single- and multiagent approaches to forming reasoning and controlling robot behavior. The evaluation results were obtained on a set of reasoning and instructional Large Language Models, such as Deepseek, Gemini, GPT, Mistral, and Qwen. The analysis of the results demonstrates that, in general, the proposed agent-based approaches are capable of effectively controlling the robot’s behavior. The developed agents, built on the basis of the most productive Large Language Models, achieve a success rate of up to 74%, with each approach incorporating its own hallucination reduction mechanism.</p>

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Agent-Based Control of Robot Behavior Using Reasoning Language Models

  • 期刊论文

  • 发表日期：

    2025年2月1日

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  • M. S. Skorokhodov,
  • V. E. Latalin,
  • R. B. Rybka,
  • A. G. Sboev

摘要

Abstract

Advances in language models open up opportunities for the creation of intelligent robot-control systems capable of interpreting general, often vaguely formulated, human target commands and shaping robot behavior based on them under given conditions. The paper proposes a system for the reasoned control of the robot’s behavior, which is the result of logical processing of goal-setting instructions using external information from the robot’s environment, taking into account feedback from the operator. Experiments were conducted to evaluate the success rate of a system of single- and multiagent approaches to forming reasoning and controlling robot behavior. The evaluation results were obtained on a set of reasoning and instructional Large Language Models, such as Deepseek, Gemini, GPT, Mistral, and Qwen. The analysis of the results demonstrates that, in general, the proposed agent-based approaches are capable of effectively controlling the robot’s behavior. The developed agents, built on the basis of the most productive Large Language Models, achieve a success rate of up to 74%, with each approach incorporating its own hallucination reduction mechanism.