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RECENT框架使小型语言模型能够实现具身智能体技能的接地

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了RECENT（RECENT）框架，旨在利用小型语言模型（sLMs）来改进具身智能体的技能接地。该方法将技能视为可执行代码，在通过局部代码重构适应特定具身和环境条件的同时，保留语义意图。RECENT在各种机器人具身和动态环境中展示了稳健的长时程性能，其性能可与大型语言模型相媲美，同时利用了更受限的sLMs。 AI

影响 通过使用更小的模型提高技能适应性，从而能够更有效地在现实场景中部署具身智能体。

排序理由 学术论文，详细介绍了使用小型语言模型在具身智能体中进行技能接地的框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

RECENT框架使小型语言模型能够实现具身智能体技能的接地

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学术论文，详细介绍了使用小型语言模型在具身智能体中进行技能接地的框架。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Sera Choi, Wonje Choi, Saehun Chun, Daehee Lee, Jooyoung Kim, Chaeun Lee, Honguk Woo ·

    利用小型语言模型通过代码重构实现高效技能基础

    arXiv:2606.07999v1 Announce Type: new Abstract: Effective skill grounding is essential for deploying reusable skills in embodied agents, as even minor embodiment or environmental differences can render an entire skill incompatible. This challenge is particularly pronounced in emb…

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