# GraphEQA: Using 3D Semantic Scene Graphs for Real-time Embodied Question Answering

arXiv: https://arxiv.org/abs/2412.14480 · HTML: https://mlsanity.com/paper/2412.14480/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 56 of its 63 references that are in the map.
- First posted on arXiv 19 Dec 2024.

## Builds on (most cited first, 30 of 56)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [Code as Policies: Language Model Programs for Embodied Control](https://mlsanity.com/paper/2209.07753/) (2022)
- [Inner Monologue: Embodied Reasoning through Planning with Language Models](https://mlsanity.com/paper/2207.05608/) (2022)
- [Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents](https://mlsanity.com/paper/2201.07207/) (2022)
- [Detecting Twenty-thousand Classes using Image-level Supervision](https://mlsanity.com/paper/2201.02605/) (2022)
- [Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI](https://mlsanity.com/paper/2109.08238/) (2021)
- [OpenScene: 3D Scene Understanding with Open Vocabularies](https://mlsanity.com/paper/2211.15654/) (2022)
- [LERF: Language Embedded Radiance Fields](https://mlsanity.com/paper/2303.09553/) (2023)
- [Habitat 2.0: Training Home Assistants to Rearrange their Habitat](https://mlsanity.com/paper/2106.14405/) (2021)
- [LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models](https://mlsanity.com/paper/2212.04088/) (2022)
- [LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action](https://mlsanity.com/paper/2207.04429/) (2022)
- [Visual Language Maps for Robot Navigation](https://mlsanity.com/paper/2210.05714/) (2022)
- [ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning](https://mlsanity.com/paper/2309.16650/) (2023)
- [Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models](https://mlsanity.com/paper/2402.07865/) (2024)
- [Embodied Question Answering](https://mlsanity.com/paper/1711.11543/) (2017)
- [ConceptFusion: Open-set Multimodal 3D Mapping](https://mlsanity.com/paper/2302.07241/) (2023)
- [3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera](https://mlsanity.com/paper/1910.02527/) (2019)
- [NaVid: Video-based VLM Plans the Next Step for Vision-and-Language Navigation](https://mlsanity.com/paper/2402.15852/) (2024)
- [TidyBot: Personalized Robot Assistance with Large Language Models](https://mlsanity.com/paper/2305.05658/) (2023)
- [Learning 3D Semantic Scene Graphs from 3D Indoor Reconstructions](https://mlsanity.com/paper/2004.03967/) (2020)
- [VLFM: Vision-Language Frontier Maps for Zero-Shot Semantic Navigation](https://mlsanity.com/paper/2312.03275/) (2023)
- [Neural Feature Fusion Fields: 3D Distillation of Self-Supervised 2D Image Representations](https://mlsanity.com/paper/2209.03494/) (2022)
- [Hierarchical Open-Vocabulary 3D Scene Graphs for Language-Grounded Robot Navigation](https://mlsanity.com/paper/2403.17846/) (2024)
- [PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs](https://mlsanity.com/paper/2402.07872/) (2024)
- [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)
- [SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences](https://mlsanity.com/paper/2103.14898/) (2021)

## Built on by (most cited first, 30 of 37)

- [Beyond the Destination: A Novel Benchmark for Exploration-Aware Embodied Question Answering](https://mlsanity.com/paper/2503.11117/) (2025)
- [Describe Anything Anywhere At Any Moment](https://mlsanity.com/paper/2512.00565/) (2025)
- [Enter the Mind Palace: Reasoning and Planning for Long-term Active Embodied Question Answering](https://mlsanity.com/paper/2507.12846/) (2025)
- [Memory-Centric Embodied Question Answering](https://mlsanity.com/paper/2505.13948/) (2025)
- [Qwen-RobotNav Technical Report: A Scalable Navigation Model Designed for an Agentic Navigation System](https://mlsanity.com/paper/2606.18112/) (2026)
- [Meta-Memory: Retrieving and Integrating Semantic-Spatial Memories for Robot Spatial Reasoning](https://mlsanity.com/paper/2509.20754/) (2025)
- [NavWAM: A Navigation World Action Model for Goal-Conditioned Visual Navigation](https://mlsanity.com/paper/2606.13494/) (2026)
- [Toward Ambulatory Vision: Learning Visually-Grounded Active View Selection](https://mlsanity.com/paper/2512.13250/) (2025)
- [STRIVE: Structured Representation Integrating VLM Reasoning for Efficient Object Navigation](https://mlsanity.com/paper/2505.06729/) (2025)
- [Vision to Geometry: 3D Spatial Memory for Sequential Embodied MLLM Reasoning and Exploration](https://mlsanity.com/paper/2512.02458/) (2025)
- [RieMind: Geometry-Grounded Spatial Agent for Scene Understanding](https://mlsanity.com/paper/2603.15386/) (2026)
- [3D Scene Graphs: Open Challenges and Future Directions](https://mlsanity.com/paper/2606.19383/) (2026)
- [GraphPad: Inference-Time 3D Scene Graph Updates for Embodied Question Answering](https://mlsanity.com/paper/2506.01174/) (2025)
- [Multi-Step Reasoning for Embodied Question Answering via Tool Augmentation](https://mlsanity.com/paper/2510.20310/) (2025)
- [HIMM: Human-Inspired Long-Term Memory Modeling for Embodied Exploration and Question Answering](https://mlsanity.com/paper/2602.15513/) (2026)
- [Stop Wandering: Efficient Vision-Language Navigation via Metacognitive Reasoning](https://mlsanity.com/paper/2604.02318/) (2026)
- [E3VS-Bench: A Benchmark for Viewpoint-Dependent Active Perception in 3D Gaussian Splatting Scenes](https://mlsanity.com/paper/2604.17969/) (2026)
- [Remember with Confidence: Uncertainty Quantification for Spatio-temporal Memory with Probabilistic Guarantees](https://mlsanity.com/paper/2606.08277/) (2026)
- [Prune-Then-Plan: Step-Level Calibration for Stable Frontier Exploration in Embodied Question Answering](https://mlsanity.com/paper/2511.19768/) (2025)
- [R4: Retrieval-Augmented Reasoning for Vision-Language Models in 4D Spatio-Temporal Space](https://mlsanity.com/paper/2512.15940/) (2025)
- [Offload or Overload: A Platform Measurement Study of Mobile Robotic Manipulation Workloads](https://mlsanity.com/paper/2603.18284/) (2026)
- [Towards Next-Generation Healthcare: A Survey of Medical Embodied AI for Perception, Decision-Making, and Action](https://mlsanity.com/paper/2606.15647/) (2026)
- [M3DMap: Object-aware Multimodal 3D Mapping for Dynamic Environments](https://mlsanity.com/paper/2508.17044/) (2025)
- [SpatialReasoner: Active Perception for Large-Scale 3D Scene Understanding](https://mlsanity.com/paper/2512.03284/) (2025)
- [Memory-Guided View Refinement for Dynamic Human-in-the-loop EQA](https://mlsanity.com/paper/2603.09541/) (2026)
- [Meanings and Measurements: Multi-Agent Probabilistic Grounding for Vision-Language Navigation](https://mlsanity.com/paper/2603.19166/) (2026)
- [ObsGraph: Hierarchical Observation Representation for Embodied Reasoning and Exploration](https://mlsanity.com/paper/2606.24068/) (2026)
- [Automating the Design of Embodied Agent Architectures](https://mlsanity.com/paper/2606.30111/) (2026)
- [DeWorldSG: Depth-Aware 3D Semantic Scene Graph Generation via World-Model Priors](https://mlsanity.com/paper/2607.00889/) (2026)
- [Scaffolding Foundation Models into Physical-World Agents Pushes the Frontier of Long-Horizon Navigation](https://mlsanity.com/paper/2608.30396/) (2026)

## Abstract (arXiv)

In Embodied Question Answering (EQA), agents must explore and develop a semantic understanding of an unseen environment to answer a situated question with confidence. This problem remains challenging in robotics, due to the difficulties in obtaining useful semantic representations, updating these representations online, and leveraging prior world knowledge for efficient planning and exploration. To address these limitations, we propose GraphEQA, a novel approach that utilizes real-time 3D metric-semantic scene graphs (3DSGs) and task relevant images as multi-modal memory for grounding Vision-Language Models (VLMs) to perform EQA tasks in unseen environments. We employ a hierarchical planning approach that exploits the hierarchical nature of 3DSGs for structured planning and semantics-guided exploration. We evaluate GraphEQA in simulation on two benchmark datasets, HM-EQA and OpenEQA, and demonstrate that it outperforms key baselines by completing EQA tasks with higher success rates and fewer planning steps. We further demonstrate GraphEQA in multiple real-world home and office environments.