RAVEN: Long-Horizon Reasoning & Navigation with a Visuo-Spatio-Temporal Memory
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Computer Science > Robotics
arXiv:2606.25206 (cs)
[Submitted on 23 Jun 2026]
Title:RAVEN: Long-Horizon Reasoning & Navigation with a Visuo-Spatio-Temporal Memory
Authors:Yixun Hu, Zhicheng Zheng, Lihan Zha, Chunwei Xing, Rajdeep Singh, Omar Hossain, Antonio Loquercio, Dhruv Shah
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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.
| Comments: | Project website: this https URL |
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.25206 [cs.RO] |
| (or arXiv:2606.25206v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2606.25206
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled RAVEN: Long-Horizon Reasoning & Navigation with a Visuo-Spatio-Temporal Memory, by Yixun Hu and 7 other authors
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