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Artificial Intelligence has made great progress in the area of Natural Language Processing, in the area of Human-Robot Interaction. The use of controlled robot language is an essential component in the process of enabling robots to comprehend and carry out human directions with pinpoint accuracy. The purpose of this work is to provide a framework that utilizes supervised fine-tuning of large language models in order to increase the accuracy of translation from natural language to Controlled Robot Language. This strategy considerably improves the dependability and effectiveness of human-robot interactions, as demonstrated by our extensive experimental investigation. The results of this study suggest that our methodology has the potential to result in more reliable robotic systems, which would be beneficial to the field of Human-Robot Interaction.

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Complex Instruction Translation Using Fine-Tuned Large Language Models

  • 会议论文

  • 发表日期：

    2025年

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  • Minhazul Arefin,
  • Dang Tran,
  • Hongsheng He

摘要

Artificial Intelligence has made great progress in the area of Natural Language Processing, in the area of Human-Robot Interaction. The use of controlled robot language is an essential component in the process of enabling robots to comprehend and carry out human directions with pinpoint accuracy. The purpose of this work is to provide a framework that utilizes supervised fine-tuning of large language models in order to increase the accuracy of translation from natural language to Controlled Robot Language. This strategy considerably improves the dependability and effectiveness of human-robot interactions, as demonstrated by our extensive experimental investigation. The results of this study suggest that our methodology has the potential to result in more reliable robotic systems, which would be beneficial to the field of Human-Robot Interaction.