[论文解读] Large Language Models for Robotics: Opportunities, Challenges, and Perspectives

Jiaqi Wang, Zihao Wu+18 more|ArXiv.org|Jan 9, 2024

Multimodal Machine Learning Applications被引用 19

一句话总结

A comprehensive survey of how large language models (LLMs) and multimodal LLMs (notably GPT-4V) are integrated into robotics for planning, manipulation, and reasoning, with frameworks, challenges, and future directions.

ABSTRACT

Large language models (LLMs) have undergone significant expansion and have been increasingly integrated across various domains. Notably, in the realm of robot task planning, LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions. However, for embodied tasks, where robots interact with complex environments, text-only LLMs often face challenges due to a lack of compatibility with robotic visual perception. This study provides a comprehensive overview of the emerging integration of LLMs and multimodal LLMs into various robotic tasks. Additionally, we propose a framework that utilizes multimodal GPT-4V to enhance embodied task planning through the combination of natural language instructions and robot visual perceptions. Our results, based on diverse datasets, indicate that GPT-4V effectively enhances robot performance in embodied tasks. This extensive survey and evaluation of LLMs and multimodal LLMs across a variety of robotic tasks enriches the understanding of LLM-centric embodied intelligence and provides forward-looking insights toward bridging the gap in Human-Robot-Environment interaction.

研究动机与目标

• Survey and synthesize existing literature on LLMs for robotic planning, manipulation, and reasoning.
• Summarize technical approaches enabling generalized robot strategies.
• Assess multimodal GPT-4V's effectiveness in embodied robotic task planning across environments.
• Provide forward-looking insights for Human-Robot-Environment interaction and embodied intelligence.

提出的方法

• Review existing work on LLMs in robotics across planning, manipulation, and reasoning tasks.
• Analyze multimodal task planning approaches such as vision-language models and frameworks like Inner Monologue and SayCan.
• Summarize modular, interactive, and reasoning-based methods integrating LLMs with classical planners and sensors.
• Propose a GPT-4V empowered embodied task planning framework and evaluate on diverse datasets.

实验结果

研究问题

• RQ1 How can LLMs be leveraged to translate natural language instructions into executable robotic plans?
• RQ2 What are the key multimodal strategies that enable robust embodied task planning in robotics?
• RQ3 What frameworks (e.g., Inner Monologue, SayCan) improve planning and execution in varying environments?
• RQ4 What are the main challenges and future directions for LLM-centric embodied intelligence in human-robot-environment interaction?

主要发现

• LLMs can generate action sequences from language, and their performance improves with visual input for visual-semantic planning.
• Multimodal models like GPT-4V enhance embodied task planning by aligning natural language with robot perception.
• Modular, interactive, and programmatic approaches (e.g., PROGRAMPORT, NLMap, SayCan extensions) improve adaptation to new tools and open-world tasks.
• Frameworks combining language, vision, and action models reduce reliance on heavy trajectory annotations and improve planning in complex scenarios.
• Human-robot interaction benefits from language-driven interfaces and grounded planning approaches that leverage external feedback and uncertainty modeling.