Beyond Episodic Evaluation: Memory Architectural Bottlenecks in Sequential Embodied Question Answering

2026-07-23

AI Summary

This work addresses the limitations of traditional Embodied Question Answering (EQA), which relies on episodic evaluation and struggles to support real-world robots in continuous tasks requiring accumulation and reuse of prior knowledge. Focusing on continuous, multi-turn EQA scenarios, the study systematically investigates the impact of memory architectures on embodied agent performance and proposes a structured, spatially anchored visual memory mechanism that maps persistent observations into a metric 3D geometric space. The approach reveals, for the first time, fundamental bottlenecks in existing memory designs and demonstrates that 3D geometry–based spatial anchoring is crucial for overcoming the trade-off between answer accuracy and navigation efficiency. Experiments show that the proposed architecture significantly improves accuracy while reducing navigation cost in simulation, and its effectiveness in continuous intelligent interaction is further validated on a real mobile robot.

Abstract

Embodied question answering (EQA) is traditionally evaluated under an episodic formulation, where agents solve each task independently and reset internal state between episodes. However, real-world robots operate continuously and must accumulate, retain, and selectively reuse information acquired from prior interactions. Despite this practical requirement, the architectural mechanisms needed to support sequential memory in EQA remain underexplored. In this work, we investigate how different memory architectures behave when EQA agents are evaluated sequentially, with multiple questions answered in the same scene while memory is carried forward across queries. We find that simply preserving existing memory is often insufficient. Agents that retain only traversability information, such as 2D occupancy maps, remember where the robot has explored but not the visual-semantic evidence needed for later questions. Agents trained on short-horizon episodic data face a different challenge: when exposed to continuous, multi-query histories, their inherited context suffers from severe temporal mismatch, rather than forming a reusable scene representation. To overcome this architectural bottleneck, we highlight the necessity of structured, spatially grounded memory: architectures that map persistent visual observations onto metric 3D geometry preserve visual-semantic evidence in a coherent scene representation. Extensive experiments in simulated environments reveal that this form of memory breaks the accuracy-efficiency tradeoff in sequential settings, simultaneously achieving higher answer accuracy and lower navigation costs. We further validate these findings on a real-world mobile robot, demonstrating that spatially grounded visual memory is critical for enabling continuous, intelligent operation in physical environments.

Problem

• Embodied Question Answering
• Sequential Memory
• Memory Architecture
• Visual-Semantic Evidence
• Scene Representation

Innovation

• structured memory
• spatially grounded memory
• sequential embodied question answering
• 3D scene representation
• continuous operation