# Meta-Memory: Retrieving and Integrating Semantic-Spatial Memories for Robot Spatial Reasoning arXiv: https://arxiv.org/abs/2509.20754 ยท HTML: https://mlsanity.com/paper/2509.20754/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning. - Builds on 29 of its 34 references that are in the map. - First posted on arXiv 25 Sep 2025. ## Placed in - LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/) ## Builds on (most cited first, 29 of 29) - [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021) - [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023) - [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025) - [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024) - [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024) - [VILA: On Pre-training for Visual Language Models](https://mlsanity.com/paper/2312.07533/) (2023) - [Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces](https://mlsanity.com/paper/2412.14171/) (2024) - [OpenScene: 3D Scene Understanding with Open Vocabularies](https://mlsanity.com/paper/2211.15654/) (2022) - [ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning](https://mlsanity.com/paper/2309.16650/) (2023) - [Visual Language Maps for Robot Navigation](https://mlsanity.com/paper/2210.05714/) (2022) - [LightRAG: Simple and Fast Retrieval-Augmented Generation](https://mlsanity.com/paper/2410.05779/) (2024) - [PaliGemma 2: A Family of Versatile VLMs for Transfer](https://mlsanity.com/paper/2412.03555/) (2024) - [Hierarchical Open-Vocabulary 3D Scene Graphs for Language-Grounded Robot Navigation](https://mlsanity.com/paper/2403.17846/) (2024) - [FAST-LIO2: Fast Direct LiDAR-inertial Odometry](https://mlsanity.com/paper/2107.06829/) (2021) - [Hydra: A Real-time Spatial Perception System for 3D Scene Graph Construction and Optimization](https://mlsanity.com/paper/2201.13360/) (2022) - [PLA: Language-Driven Open-Vocabulary 3D Scene Understanding](https://mlsanity.com/paper/2211.16312/) (2022) - [CLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory](https://mlsanity.com/paper/2210.05663/) (2022) - [NavGPT-2: Unleashing Navigational Reasoning Capability for Large Vision-Language Models](https://mlsanity.com/paper/2407.12366/) (2024) - [FAST-LIO: A Fast, Robust LiDAR-inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter](https://mlsanity.com/paper/2010.08196/) (2020) - [3D-Mem: 3D Scene Memory for Embodied Exploration and Reasoning](https://mlsanity.com/paper/2411.17735/) (2024) - [ReMEmbR: Building and Reasoning Over Long-Horizon Spatio-Temporal Memory for Robot Navigation](https://mlsanity.com/paper/2409.13682/) (2024) - [TrackVLA: Embodied Visual Tracking in the Wild](https://mlsanity.com/paper/2505.23189/) (2025) - [Embodied-RAG: General Non-parametric Embodied Memory for Retrieval and Generation](https://mlsanity.com/paper/2409.18313/) (2024) - [GraphEQA: Using 3D Semantic Scene Graphs for Real-time Embodied Question Answering](https://mlsanity.com/paper/2412.14480/) (2024) - [OpenGraph: Open-Vocabulary Hierarchical 3D Graph Representation in Large-Scale Outdoor Environments](https://mlsanity.com/paper/2403.09412/) (2024) - [NavRAG: Generating User Demand Instructions for Embodied Navigation through Retrieval-Augmented LLM](https://mlsanity.com/paper/2502.11142/) (2025) - [NeuPAN: Direct Point Robot Navigation with End-to-End Model-based Learning](https://mlsanity.com/paper/2403.06828/) (2024) - [DynamicGSG: Dynamic 3D Gaussian Scene Graphs for Environment Adaptation](https://mlsanity.com/paper/2502.15309/) (2025) - [RTAGrasp: Learning Task-Oriented Grasping from Human Videos via Retrieval, Transfer, and Alignment](https://mlsanity.com/paper/2409.16033/) (2024) ## Built on by (most cited first, 9 of 9) - [Goal2Skill: Long-Horizon Manipulation with Adaptive Planning and Reflection](https://mlsanity.com/paper/2604.13942/) (2026) - [BrainMem: Brain-Inspired Evolving Memory for Embodied Agent Task Planning](https://mlsanity.com/paper/2604.16331/) (2026) - [Semantic Flip: Synthetic OOD Generation for Robust Refusal in Embodied Question Answering and Spatial Localization](https://mlsanity.com/paper/2606.16898/) (2026) - [RAVEN: Long-Horizon Reasoning & Navigation with a Visuo-Spatio-Temporal Memory](https://mlsanity.com/paper/2606.25206/) (2026) - [Binary Tracking for Spatial QA and Navigation with Open Vision-Language Models](https://mlsanity.com/paper/2606.16902/) (2026) - [Mosaic: Runtime-Efficient Multi-Agent Embodied Planning](https://mlsanity.com/paper/2607.09603/) (2026) - [EmbodiedLGR: Integrating Lightweight Graph Representation and Retrieval for Semantic-Spatial Memory in Robotic Agents](https://mlsanity.com/paper/2604.18271/) (2026) - [A Semantic Autonomy Framework for VLM-Integrated Indoor Mobile Robots: Hybrid Deterministic Reasoning and Cross-Robot Adaptive Memory](https://mlsanity.com/paper/2605.02525/) (2026) - [Spatial and Semantic Reasoning for LLM-Driven Robot Navigation via MCP](https://mlsanity.com/paper/2609.27340/) (2026) ## Abstract (arXiv) 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/ .