让机器人通过语义与空间联合推理，精准回答位置问题。
Meta-Memory: Retrieving and Integrating Semantic-Spatial Memories for Robot Spatial Reasoning
- 用大模型构建高密度环境记忆，支持多模态联合推理。
- 在真实场景中问答准确率显著超越现有方法。
- 适合需要空间理解的智能机器人研发人员使用。
导航复杂环境要求机器人有效存储观察结果为记忆，并利用这些记忆回答人类关于空间位置的提问，这是一个关键但研究不足的挑战。尽管已有工作在构建机器人记忆方面取得进展，但针对高效记忆检索与整合的系统性机制仍缺乏。为此，我们提出Meta-Memory，一种由大语言模型驱动的代理系统，能够构建环境的高密度记忆表征。其核心创新在于可通过自然语言位置查询，联合推理语义与空间模态，实现相关记忆的检索与融合，从而赋予机器人强大的空间推理能力。为评估性能，我们引入SpaceLocQA，一个涵盖多样化真实世界空间问答场景的大规模数据集。实验结果表明，Meta-Memory在SpaceLocQA和公开的NaVQA基准上均显著优于现有先进方法。此外，我们在真实机器人平台上成功部署了Meta-Memory，验证了其在复杂环境中的实用价值。
原文摘要 · Abstract (English)
Navigating complex environments requires robots to effectively store observations as memories and leverage them to answer human queries about spatial locations, which is a critical yet underexplored research challenge. While prior work has made progress in constructing robotic memory, few have addressed the principled mechanisms needed for efficient memory retrieval and integration. To bridge this gap, we propose Meta-Memory, a large language model (LLM)-driven agent that constructs a high-density memory representation of the environment. The key innovation of Meta-Memory lies in its capacity to retrieve and integrate relevant memories through joint reasoning over semantic and spatial modalities in response to natural language location queries, thereby empowering robots with robust and accurate spatial reasoning capabilities. To evaluate its performance, we introduce SpaceLocQA, a large-scale dataset encompassing diverse real-world spatial question-answering scenarios. Experimental results show that Meta-Memory significantly outperforms state-of-the-art methods on both the SpaceLocQA and the public NaVQA benchmarks. Furthermore, we successfully deployed Meta-Memory on real-world robotic platforms, demonstrating its practical utility in complex environments. Project page: https://itsbaymax.github.io/meta-memory.github.io/ .
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。