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RoboGPT: An LLM-Based Long-Term Decision-Making Embodied Agent for Instruction Following Tasks

RoboGPT: An LLM-Based Long-Term Decision-Making Embodied Agent for Instruction Following Tasks

Section titled “RoboGPT: An LLM-Based Long-Term Decision-Making Embodied Agent for Instruction Following Tasks”

⚠️ AI 生成 · 建议对照原文 本页为自动整理的学习笔记;关键数据与引用如需引用,请回查 PDF / 官方版本。

学习档位 自动卡

类型 文献 · 更新 2026-07-19

所属 LLM 与语言推理 · 决策与任务规划

  • topic: llm-language-reasoning
  • sources: openalex
  • retrieved_at: 2026-07-20
  • query: large language model reasoning robotics planning
  • doi: 10.1109/tcds.2025.3543364
  • score_total: 68
  • suggested_tier: recent

(no prose relevance explanation — numeric score only or HTTP source)

(no snippet evidence in candidate pool)

AI analysis (heuristic fallback, needs-source-verification)

Section titled “AI analysis (heuristic fallback, needs-source-verification)”

Status: metadata_only · Source: metadata

Metadata-only card for RoboGPT: An LLM-Based Long-Term Decision-Making Embodied Agent for Instruction Following Tasks.

生成:2026-07-21 · 来源条数 0 · 模型 heuristic · 需人工核验数字

围绕「RoboGPT: An LLM-Based Long-Term Decision-Making Embodied Agent for Instruction Following Tasks」的核心问题与动机(待结合全文核验)。

  • 见原文方法章节;以下为基于摘要/摘录的要点提示。
  • 本地摘录暂缺。

与相近工作的关系待核验;请对照 related work。

  • 勿仅凭摘要推断未给出的数值指标。
  1. 这篇工作的输入/输出表示是什么?(RoboGPT: An LLM-Based Long-Term Decision-Making Embodied Agent for Instruction Following Tasks)
  2. 训练目标与评测协议各是什么?
  3. 主要失败模式或局限是什么?
  • (本次未抓取到白名单二次解读页)
flowchart LR
A["输入 / 观测"] --> B["表示 / 编码"]
B --> C["推理 / 解码"]
C --> D["输出 / 动作或检测"]
%% method sketch for: RoboGPT: An LLM-Based Long-Term Decision-Making Embodied Agent for Instruction F

方法结构示意(重绘;细节以原论文为准,待 PDF 核验)。

展开英文 Paper Card / AI deep analysis
Field Content
Year 2025
Authors Yaran Chen, Wenbo Cui, Yuanwen Chen, Mining Tan, Zhang Xinyao, Jinrui Liu, Haoran Li, Dongbin Zhao, He Wang
arXiv
DOI 10.1109/tcds.2025.3543364
Topics llm-language-reasoning, decision-task-planning
展开 Extract / Selections / Local assets
  • llm-language-reasoning: tier=foundational rank=1 score=68 — auto refresh 2026-07-19 sources=openalex | promoted recent->foundational for coverage fill
  • decision-task-planning: tier=watch rank=2 score=63 — cross-topic assign from registry title match=3 keywords; 2026-07-19
(no PDF text available; metadata-only card)