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  3. Streamlining Human-robot Interaction: Integrating Llm-based Planning Into Modular Robotic Frameworks.
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  3. Streamlining Human-robot Interaction: Integrating Llm-based Planning Into Modular Robotic Frameworks.

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Streamlining Human-Robot Interaction: Integrating LLM-Based Planning into Modular Robotic Frameworks.

MinHyuk Kim1, JooHee Park2, Kwanyong Park1

  • 1Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of Korea.

Sensors (Basel, Switzerland)
|March 28, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study enhances embodied artificial intelligence (AI) by improving spoken instruction understanding and reducing task times. The new Module Handler system achieves 92.47% accuracy and cuts execution time by 33 seconds.

Keywords:
embodied AIhuman–robot interactionlarge language modelmodular roboticstask execution

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Human-Robot Interaction

Background:

  • Embodied artificial intelligence (AI) shows promise in tasks like household chores and object manipulation.
  • Current systems struggle with slow execution and user instruction difficulties.
  • Real-world applicability of AI robots is limited by these challenges.

Purpose of the Study:

  • To enhance the usability of embodied AI through spoken instructions.
  • To reduce robot operation time by streamlining intermediate steps.
  • To improve the efficiency of robotic task execution.

Main Methods:

  • Leveraging a large language model (LLM) for accurate information extraction from spoken human instructions.
  • Implementing a Module Handler to streamline intermediate steps in robotic tasks.
  • Conducting experimental validation of the proposed approach.

    • Main Results:

    • Achieved an object identification accuracy rate of approximately 92.47% for spoken instructions.
    • Reduced task completion times by an average of 33 seconds across four experimental environments.
    • Demonstrated significant improvements in robotic task execution efficiency compared to existing systems.

    Conclusions:

    • The proposed approach effectively enhances embodied AI usability and efficiency.
    • LLM-powered instruction understanding and streamlined execution are key to practical AI robotics.
    • This advancement paves the way for more practical and efficient AI-driven robotic systems.