English(EN) Probing Embodied LLMs: When Higher Observation Fidelity Hurts Problem Solving

具身大语言模型在处理嘈杂、原始视觉输入时表现优于完美数据

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-19 16:27

一项新的研究论文探讨了大语言模型（LLMs）集成到机器人系统中后在复杂任务上的表现。研究发现，为LLMs提供原始RGB视觉输入比提供完美的、真实标记的符号化观察结果更能解决问题。与直觉相反，在观察结果中引入适度的噪声或随机错误实际上提高了LLMs的性能，减少了重复性动作循环并提高了成功率。 AI

影响表明当前具身大语言模型的评估指标可能具有误导性，因为性能可以通过感知错误而不是强大的问题解决能力来提升。

排序理由在arXiv上发表的研究论文，详细介绍了LLM在具身任务中的行为实验结果。[lever_c_demoted from research: ic=1 ai=1.0]

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Newsworthiness bucket: Tool; 在arXiv上发表的研究论文，详细介绍了LLM在具身任务中的行为实验结果。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.AI TIER_1 English(EN) · Oliver Brock · 2026-05-19 16:27

探索具身大语言模型：当更高的观察保真度损害问题解决能力

Large Language Models are increasingly proposed as cognitive components for robotic systems, yet their opaque decision processes make it difficult to explain success or failure in closed-loop embodied tasks. Following an empirical AI methodology, we study embodied LLM agents beha…