arXiv:2606.25206v1 Announce Type: cross 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 captio
Advances in visual AI and spatial reasoning are enabling more robust long-term autonomous systems, addressing critical memory and navigation challenges for robot deployment.
This development represents a significant step towards practical, long-duration robot autonomy, crucial for complex tasks beyond controlled environments.
Robots can now operate with more sophisticated memory systems, allowing for sustained, complex interactions and reasoning over extended periods rather than just short-term tasks.
- · robotics companies
- · logistics sector
- · AI developers
- · manual labor in hazardous environments
- · companies relying on limited-autonomy robots
More capable and persistent autonomous robots become viable for industrial and service applications.
Increased adoption of autonomous robot fleets for monitoring, maintenance, and complex navigation tasks in diverse environments.
The proliferation of contextually aware robots could lead to new forms of human-robot collaboration and resource optimization across industries.
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Read at arXiv cs.CL