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Embodied RAG++: Retrieval-Augmented Generation From What Exists to What Works in Embodied Scenes

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This paper introduces E2W-Bench, a benchmark linking dynamic hybrid scene graphs with real product manuals to evaluate capability-grounded robot-object assignment under constraints like availability and vertical reach. The authors also present Embodied RAG++, a spatially grounded retrieval framework that scopes retrieval to candidate object manuals and resolves feasibility through deterministic spatial reasoning. On E2W-Bench, Embodied RAG++ achieves a pair accuracy of 0.90 with Qwen2.5-32B and 1.00 with Llama3.3-70B, outperforming baselines which reached at most 0.30 and 0.55 respectively, while requiring the shortest preprocessing time among retrieval-based methods.

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Latest development2026-11-06 05:00 UTC
Embodied RAG++: Retrieval-Augmented Generation From What Exists to What Works in Embodied Scenes

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11/6
  1. arXiv Robotics — research abstractsSignal
    Embodied RAG++: Retrieval-Augmented Generation From What Exists to What Works in Embodied Scenes

    This paper introduces E2W-Bench, a benchmark linking dynamic hybrid scene graphs with real product manuals to evaluate capability-grounded robot-object assignment under constraints like availability and vertical reach. The authors also present Embodied RAG++, a spatially grounded retrieval framework that scopes retrieval to candidate object manuals and resolves feasibility through deterministic spatial reasoning. On E2W-Bench, Embodied RAG++ achieves a pair accuracy of 0.90 with Qwen2.5-32B and 1.00 with Llama3.3-70B, outperforming baselines which reached at most 0.30 and 0.55 respectively, while requiring the shortest preprocessing time among retrieval-based methods.

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