One to rule them all: natural language to bind communication, perception and action

• URL: http://arxiv.org/abs/2411.15033v1
• Date: Fri, 22 Nov 2024 16:05:54 GMT
• Title: One to rule them all: natural language to bind communication, perception and action
• Authors: Simone Colombani, Dimitri Ognibene, Giuseppe Boccignone,
• Abstract summary: This paper presents an advanced architecture for robotic action planning that integrates communication, perception, and planning with Large Language Models (LLMs) The Planner Module is the core of the system where LLMs embedded in a modified ReAct framework are employed to interpret and carry out user commands. The modified ReAct framework further enhances the execution space by providing real-time environmental perception and the outcomes of physical actions.
• Score: 0.9302364070735682
• License:
• Abstract: In recent years, research in the area of human-robot interaction has focused on developing robots capable of understanding complex human instructions and performing tasks in dynamic and diverse environments. These systems have a wide range of applications, from personal assistance to industrial robotics, emphasizing the importance of robots interacting flexibly, naturally and safely with humans. This paper presents an advanced architecture for robotic action planning that integrates communication, perception, and planning with Large Language Models (LLMs). Our system is designed to translate commands expressed in natural language into executable robot actions, incorporating environmental information and dynamically updating plans based on real-time feedback. The Planner Module is the core of the system where LLMs embedded in a modified ReAct framework are employed to interpret and carry out user commands. By leveraging their extensive pre-trained knowledge, LLMs can effectively process user requests without the need to introduce new knowledge on the changing environment. The modified ReAct framework further enhances the execution space by providing real-time environmental perception and the outcomes of physical actions. By combining robust and dynamic semantic map representations as graphs with control components and failure explanations, this architecture enhances a robot adaptability, task execution, and seamless collaboration with human users in shared and dynamic environments. Through the integration of continuous feedback loops with the environment the system can dynamically adjusts the plan to accommodate unexpected changes, optimizing the robot ability to perform tasks. Using a dataset of previous experience is possible to provide detailed feedback about the failure. Updating the LLMs context of the next iteration with suggestion on how to overcame the issue.

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