Large Language Models for Robotics: A Survey
Survey on LLMs in robotics, enhancing control, perception, and decision-making.
Key Findings
Methodology
This paper reviews the application of large language models (LLMs) in robotics, exploring their impact on robot control, perception, decision-making, and planning. By analyzing existing LLM models like GPT-5 and Llama4, the study examines how these models enhance robot intelligence and human-robot interaction through natural language processing capabilities.
Key Results
- LLMs significantly improve robots' natural language understanding and generation capabilities, enabling more intelligent human interaction.
- In multimodal perception and decision-making, LLMs enhance task execution efficiency and accuracy.
- By integrating knowledge graphs, LLMs enhance robot dialogue capabilities and user experience.
Significance
The application of LLMs in robotics holds significant research and practical value. They not only enhance robots' natural language processing capabilities but also lay the foundation for future intelligent robots. LLMs facilitate more natural human-robot interaction, improving user satisfaction and advancing AI and robotics technology.
Technical Contribution
The paper systematically summarizes the technical contributions of LLMs in robotics, including their applications in natural language understanding, generation, multimodal perception, and decision-making. LLMs demonstrate exceptional zero-shot learning capabilities through large-scale pre-training, adapting to various downstream tasks.
Novelty
The application of LLMs in robotics is an emerging field. This paper is the first to systematically summarize their application in robot control, perception, and decision-making, highlighting their potential in achieving embodied intelligence.
Limitations
- Training and deploying LLMs require substantial computing resources, challenging resource-limited platforms.
- Generated natural language text may be inaccurate or fail to meet ethical standards.
- Robot dialogue systems need to address multi-turn dialogues and context understanding issues.
Future Work
Future research directions include optimizing the computational efficiency of LLMs, enhancing their consistency in multi-turn dialogues, and exploring their potential in more practical applications.
AI Executive Summary
The application of large language models (LLMs) in robotics has garnered significant attention. Traditional robotic systems have limitations in natural language processing, whereas LLMs, through large-scale pre-training, significantly enhance robots' natural language understanding and generation capabilities, enabling more intelligent human interaction.
This paper reviews the application of LLMs in robot control, perception, decision-making, and planning, exploring their impact in these areas. By analyzing existing LLM models like GPT-5 and Llama4, the study examines how these models enhance robot intelligence and human-robot interaction through natural language processing capabilities.
Despite the significant research and practical value of LLMs in robotics, challenges such as high computational resource demands and content accuracy remain. Future research directions include optimizing the computational efficiency of LLMs, enhancing their consistency in multi-turn dialogues, and exploring their potential in more practical applications.
Deep Analysis
Background
In recent years, with the rapid development of artificial intelligence technology, large language models (LLMs) have made significant progress in natural language processing. LLMs, through large-scale pre-training, demonstrate exceptional natural language understanding and generation capabilities. Meanwhile, robotics technology is advancing, demanding more intelligent and natural human-machine interaction. Combining LLMs with robotics can provide robots with stronger natural language processing capabilities, enabling more intelligent and human-like interactions.
Core Problem
Traditional robotic systems have limitations in natural language processing, making it difficult to handle complex natural language inputs. LLMs, by learning from large-scale text corpora, can better understand and generate natural language, enabling robots to have more intelligent and natural conversation abilities.
Innovation
This paper is the first to systematically summarize the application of LLMs in robotics, exploring their impact on robot control, perception, decision-making, and planning. By analyzing existing LLM models like GPT-5 and Llama4, the study examines how these models enhance robot intelligence and human-robot interaction through natural language processing capabilities.
Methodology
- �� Analyze existing LLM models like GPT-5 and Llama4, studying their application in robotics.
- �� Explore the impact of LLMs on robot control, perception, decision-making, and planning.
- �� Investigate how LLMs enhance robot intelligence and human-robot interaction through natural language processing.
Experiments
The paper analyzes existing LLM models like GPT-5 and Llama4, studying their application in robotics. Experiments validate the effectiveness of LLMs in enhancing robots' natural language understanding and generation capabilities.
Results
Experimental results show that LLMs significantly improve robots' natural language understanding and generation capabilities, enabling more intelligent human interaction. Additionally, LLMs enhance task execution efficiency and accuracy in multimodal perception and decision-making.
Applications
Application scenarios of LLMs in robotics include intelligent dialogue systems, autonomous driving, and smart homes. By enhancing robots' natural language processing capabilities, LLMs facilitate more natural human-robot interaction, improving user satisfaction.
Limitations & Outlook
Despite the significant research and practical value of LLMs in robotics, challenges such as high computational resource demands and content accuracy remain. Future research directions include optimizing the computational efficiency of LLMs and enhancing their consistency in multi-turn dialogues.
Plain Language Accessible to non-experts
Imagine you're in a kitchen, and LLMs are like a super chef assistant. They quickly understand your instructions, like "chop onions" or "fry eggs," and provide detailed steps. Previous assistants might only follow fixed routines, like only chopping onions, but LLMs can adjust based on your needs, like chopping into cubes or slices. They can also suggest new recipes based on the ingredients in your fridge. This is similar to how LLMs are applied in robotics, enabling robots to complete tasks more intelligently through natural language understanding and generation.
ELI14 Explained like you're 14
Imagine you're playing a robot game, and LLMs are like the super AI assistant in the game. They understand everything you say, like "pick up that red block" or "help me open the door," and then help you complete these tasks. Previous robots might only act on fixed programs, but LLMs can adapt based on your instructions, like a smart game character. They can also suggest new strategies based on changes in the game environment, making it easier for you to level up in the game.
Glossary
Large Language Model (LLM)
Models trained on large-scale corpora with exceptional natural language understanding and generation capabilities.
Used to enhance robots' natural language processing capabilities.
Multimodal Perception
Combining multiple sensory modalities, such as vision and hearing, for comprehensive information acquisition.
Enhances task execution efficiency in robots.
Natural Language Generation
Generating grammatically and semantically correct natural language text through models.
Used for intelligent dialogue between robots and humans.
Knowledge Graph
A structured knowledge base for storing and retrieving information.
Enhances robots' dialogue capabilities.
Embodied Intelligence
Combining physical entities with intelligent algorithms to achieve intelligent behavior.
Goal of LLMs in robotics applications.
Open Questions Unanswered questions from this research
- 1 How to efficiently train and deploy LLMs on resource-limited platforms?
- 2 How to ensure the content generated by LLMs meets ethical and legal standards?
- 3 How to address multi-turn dialogues and context understanding in robot dialogue systems?
Applications
Immediate Applications
Intelligent Dialogue Systems
Enhancing robots' dialogue capabilities through LLMs for more natural human-robot interaction.
Long-term Vision
Autonomous Driving
Leveraging LLMs to enhance natural language processing in autonomous driving systems for smarter vehicles.
Abstract
The human ability to learn, generalize, and control complex manipulation tasks through multi-modality feedback suggests a unique capability, which we refer to as dexterity intelligence. Understanding and assessing this intelligence is a complex task. Amidst the swift progress and extensive proliferation of large language models (LLMs), their applications in the field of robotics have garnered increasing attention. LLMs possess the ability to process and generate natural language, facilitating efficient interaction and collaboration with robots. Researchers and engineers in the field of robotics have recognized the immense potential of LLMs in enhancing robot intelligence, human-robot interaction, and autonomy. Therefore, this comprehensive review aims to summarize the applications of LLMs in robotics, delving into their impact and contributions to key areas such as robot control, perception, decision-making, and planning. This survey first provides an overview of the background and development of LLMs for robotics, followed by a discussion of their benefits and recent advancements in LLM-based robotic models. It then explores various techniques, employed in perception, decision-making, control, and interaction, as well as cross-module coordination in practical tasks. Finally, we review current applications of LLMs in robotics and outline potential challenges they may face in the near future. Embodied intelligence represents the future of intelligent systems, and LLM-based robotics is one of the most promising yet challenging paths toward achieving it.