Using Natural Language for Human-Robot Collaboration in the Real World

2025-08-15

AI Summary

This study addresses the challenge of enabling embodied robots to collaboratively perform complex tasks with humans in real-world environments through natural language. We propose an end-to-end architecture that deeply integrates large language models (LLMs)—e.g., ChatGPT—into embodied cognitive agents. Our method unifies LLM-based language processing, contextual knowledge modeling, human-robot interaction mechanisms, and an embodied reasoning framework to achieve closed-loop coordination among dynamic language understanding, context-aware perception, and physical task execution. Crucially, we move beyond conventional command-following paradigms by enabling robots to iteratively refine language comprehension and collaborative strategies grounded in situated experience. Through three systematic proof-of-concept experiments, we empirically validate the efficacy of LLMs in multi-step instruction parsing, context-dependent reasoning, and collaborative dialogue. The results establish a reproducible technical pathway and empirical foundation for developing intelligent collaborative robots endowed with situational cognition and interactive learning capabilities.

Technology Category

• Intelligent Robots: Embodied AI
• Natural Language Processing: Safety and Robustness
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

Abstract

We have a vision of a day when autonomous robots can collaborate with humans as assistants in performing complex tasks in the physical world. This vision includes that the robots will have the ability to communicate with their human collaborators using language that is natural to the humans. Traditional Interactive Task Learning (ITL) systems have some of this ability, but the language they can understand is very limited. The advent of large language models (LLMs) provides an opportunity to greatly improve the language understanding of robots, yet integrating the language abilities of LLMs with robots that operate in the real physical world is a challenging problem. In this chapter we first review briefly a few commercial robot products that work closely with humans, and discuss how they could be much better collaborators with robust language abilities. We then explore how an AI system with a cognitive agent that controls a physical robot at its core, interacts with both a human and an LLM, and accumulates situational knowledge through its experiences, can be a possible approach to reach that vision. We focus on three specific challenges of having the robot understand natural language, and present a simple proof-of-concept experiment using ChatGPT for each. Finally, we discuss what it will take to turn these simple experiments into an operational system where LLM-assisted language understanding is a part of an integrated robotic assistant that uses language to collaborate with humans.

Problem

• Enabling robots to understand natural language for human collaboration
• Integrating large language models with physical world robotic systems
• Overcoming limited language comprehension in interactive task learning robots

Innovation

• Integrates large language models with physical robots
• Uses cognitive agent for human-robot interaction
• Employs ChatGPT for natural language understanding