用大模型自动生成机器人知识图谱，提升云机器人知识共享效率
RecipeMasterLLM: Revisiting RoboEarth in the Era of Large Language Models
- 用微调大模型根据用户指令生成符合标准的知识图谱动作描述
- 结合环境信息的检索增强生成，使动作描述更准确
- 适合研究云机器人、知识图谱与大模型融合的开发者
RoboEarth是云机器人领域的开创性项目，通过标准化知识图谱实现机器人对动作、物体和环境知识的共享。早期知识主要由工程师手工构建，使用RDF三元组和OWL本体，更新依赖机器人控制与感知模块。随着大语言模型（LLMs）的快速发展，我们提出RecipeMasterLLM——一个高层规划器，可根据用户提示自动生成符合标准知识图谱的OWL动作本体。该架构采用专门微调的LLM，理解并生成与RoboEarth标准一致的动作描述；在检索增强生成（RAG）阶段，引入环境知识以增强上下文理解，提升生成动作描述的准确性。
原文摘要 · Abstract (English)
RoboEarth was a pioneering initiative in cloud robotics, establishing a foundational framework for robots to share and exchange knowledge about actions, objects, and environments through a standardized knowledge graph. Initially, this knowledge was predominantly hand-crafted by engineers using RDF triples within OWL Ontologies, with updates, such as changes in an object's pose, being asserted by the robot's control and perception routines. However, with the advent and rapid development of Large Language Models (LLMs), we believe that the process of knowledge acquisition can be significantly automated. To this end, we propose RecipeMasterLLM, a high-level planner, that generates OWL action ontologies based on a standardized knowledge graph in response to user prompts. This architecture leverages a fine-tuned LLM specifically trained to understand and produce action descriptions consistent with the RoboEarth standardized knowledge graph. Moreover, during the Retrieval-Augmented Generation (RAG) phase, environmental knowledge is supplied to the LLM to enhance its contextual understanding and improve the accuracy of the generated action descriptions.
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