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# Paper: Agentic RAG-VLM: Affordance-Aware Retrieval-Augmented Generation with Self-Reflective Planning for Robotic Grasping
---
type: paper
title: "Agentic RAG-VLM: Affordance-Aware Retrieval-Augmented Generation with Self-Reflective Planning for Robotic Grasping"
authors: Tao Chen, Lizheng Liu, Jiaxu Wang, Ziyue Jiang, Ruiqi Tian, JiGuang Huo, Zhongxue Gan
year: 2026
venue: arXiv
url: https://arxiv.org/abs/2606.31200
code_url:
source: arxiv
collected_at: 2026-07-08
published_at: 2026-06-30
updated_at: 2026-06-30
status: queued
relevance: high
topics:
- agent-evaluation
- embodied-agent
- planning
- rag
- reasoning
methods:
-
benchmarks:
-
models:
-
datasets:
- cs.AI
related_concepts:
-
related_jobs:
-
related_experiments:
-
related_projects:
-
collection_score: 16
collection_queries: rag-agent
---
## One-line Takeaway
Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.
## Why Collected
- matched queries: rag-agent
- inferred topics: agent-evaluation, embodied-agent, planning, rag, reasoning
- arXiv categories: cs.AI
- collection score: 16
## Review Checklist
- Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
- Does it include a benchmark, dataset, code, or reproducible experimental setup?
- Should it be promoted from `queued` to `skimmed` or `summarized`?
## Links
- arXiv: https://arxiv.org/abs/2606.31200