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Integrating large language models for intuitive robot navigation.

Ziheng Xue1, Arturs Elksnis1, Ning Wang2

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Summary

This study introduces an AI agent framework using quantized Large Language Models (LLMs) to improve home assistance robots. The system enhances natural language understanding and navigation in resource-limited environments.

Keywords:
AI agentLoRA fine-tuninghome assistance robotslarge language modelsquantization

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Home assistance robots struggle with natural language interaction, object detection, and navigation in resource-constrained environments.
  • These limitations hinder the practical deployment and effectiveness of domestic robotic assistants.

Purpose of the Study:

  • To propose an AI agent framework utilizing Large Language Models (LLMs) to address challenges in home assistance robots.
  • To enable robust natural language interaction and navigation capabilities on resource-limited devices.

Main Methods:

  • Developed an AI agent framework incorporating EnvNet, RoutePlanner, and AIBrain.
  • Utilized quantized Large Language Models (LLMs) to reduce computational requirements.
  • Integrated components for natural language understanding and spatial navigation.

Main Results:

  • Demonstrated improved natural language understanding in home environments.
  • Achieved enhanced navigation accuracy for home assistance robots.
  • Showcased the feasibility of deploying LLM-based agents on resource-limited hardware.

Conclusions:

  • The proposed LLM-based AI agent framework offers a viable solution for enhancing home assistance robot capabilities.
  • Quantized LLMs are effective for enabling advanced AI functions in resource-constrained domestic settings.
  • This research provides a valuable foundation for the practical deployment of intelligent home assistance robots.