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Related Experiment Video

Updated: Mar 29, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

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

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 AI human–robot interaction large language model modular robotics task 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.