LA-RCS: LLM-Agent-Based Robot Control System

TL;DR

LA-RCS is an innovative robot control system that uses a dual-agent LLM framework to autonomously plan, execute, and adapt to environmental changes, significantly reducing user intervention with a 90% success rate.

Contribution

The paper introduces LA-RCS, a novel LLM-agent-based framework for autonomous robot control that integrates planning, execution, and environmental adaptation.

Findings

Achieved an average success rate of 90% across various scenarios.

Demonstrated effective natural language command interpretation and execution.

System reduces user intervention in robot task management.

Abstract

LA-RCS (LLM-agent-based robot control system) is a sophisticated robot control system designed to autonomously plan, work, and analyze the external environment based on user requirements by utilizing LLM-Agent. Utilizing a dual-agent framework, LA-RCS generates plans based on user requests, observes the external environment, executes the plans, and modifies the plans as needed to adapt to changes in the external conditions. Additionally, LA-RCS interprets natural language commands by the user and converts them into commands compatible with the robot interface so that the robot can execute tasks and meet user requests properly. During his process, the system autonomously evaluates observation results, provides feedback on the tasks, and executes commands based on real-time environmental monitoring, significantly reducing the need for user intervention in fulfilling requests. We categorized the scenarios that LA-RCS needs to perform into four distinct types and conducted a quantitative assessment of its performance in each scenario. The results showed an average success rate of 90 percent, demonstrating the system capability to fulfill user requests satisfactorily. For more extensive results, readers can visit our project page: https://la-rcs.github.io