Multimodal Machine Learning Applications
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Abstract
Abstract
Long-term robot deployment requires a compact and scalable memory that preserves fine-grained visual semantics, grounds observations in space and time, and enables efficient storage and retrieval. In this paper, we propose RAVEN, an agentic memory system for long-horizon robotic question answering and navigation. RAVEN stores visual embeddings with pose and time in a vector database, and grounds retrieval in a spatial map to answer queries and navigate to goals. By operating directly on visual embeddings, RAVEN avoids lossy image-to-text captioning and enables accurate semantic, spatial, and temporal retrieval at scale. Across several simulated and real-world video question-answering benchmarks, RAVEN consistently surpasses caption-based memory systems and matches frontier VLMs on long-horizon tasks at 10$\times$ lower retrieval cost. Finally, we instantiate RAVEN on a Unitree Go1 robot for the task of long-horizon navigation for natural language goal-reaching, and show successful deployment over several large indoor environments.
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@article{Hu2026RAVEN,
title = {RAVEN: Long-Horizon Reasoning & Navigation with a Visuo-Spatio-Temporal Memory},
author = {Yixun Hu and Zhicheng Zheng and Lihan Zha and Chunwei Xing and Rajdeep Singh and Omar Hossain and Antonio Loquercio and Dhruv Shah},
journal = {arXiv (Cornell University)},
year = {2026},
doi = {10.48550/arxiv.2606.25206},
url = {https://doi.org/10.48550/arxiv.2606.25206}
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