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

Saumya Saxena, Blake Buchanan, Chris Paxton, Peiqi Liu, Bingqing Chen, Narunas Vaskevicius, Luigi Palmieri, Jonathan Francis, Oliver Kroemer

Keywords: Embodied Question Answering, Vision Language Models, Robot Planning, Real-time 3D Scene Graphs, Guided Exploration

TL;DR: We propose GraphEQA, a novel approach that utilizes real-time 3D metric-semantic scene graphs (3DSG) and task relevant images as multi-modal memory for grounding Vision-Language Models (VLMs) to perform EQA tasks in unseen environments.

Abstract: In Embodied Question Answering (EQA), agents must explore and develop a semantic understanding of an unseen environment in order to answer a situated question with confidence. This remains a challenging problem in robotics, due to the difficulties in obtaining useful semantic representations, updating these representations online, and leveraging prior world knowledge for efficient exploration and planning. Aiming 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 semantic-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, and further demonstrate GraphEQA in two separate real world environments. Videos and code are available at https://grapheqa.github.io.

Submission Number: 740