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Retrieval-Augmented Robots via Retrieve-Reason-Act

arXiv – CS AI|Izat Temiraliev, Diji Yang, Yi Zhang|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers introduce Retrieval-Augmented Robotics (RAR), a new paradigm enabling robots to actively retrieve and use external visual documentation to execute complex tasks. The system uses a Retrieve-Reason-Act loop where robots search unstructured visual manuals, align 2D diagrams with 3D objects, and synthesize executable plans for assembly tasks.

Key Takeaways

→RAR transforms robots from passive executors into active information retrieval users capable of learning from external documentation.
→The system addresses critical information gaps in zero-shot scenarios where robots lack prior demonstrations or internal knowledge.
→Robots can now ground abstract 2D visual instructions to 3D physical parts through cross-modal alignment.
→Testing on long-horizon assembly tasks shows significant performance improvements over zero-shot reasoning baselines.
→This approach extends information retrieval from answering queries to driving physical robotic actions.

#retrieval-augmented-robotics #rar #robotic-planning #information-retrieval #zero-shot-learning #assembly-robots #cross-modal-alignment #embodied-ai #arxiv

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