通过代的政策改进,用大型语言模型进行感官运动控制.
Jonata Tyska Carvalho1,2, Stefano Nolfi3
1Informatics and Statistics Department, Federal University of Santa Catarina (UFSC), Florianópolis, Brazil. jonata.tyska@ufsc.br.
Scientific reports
|March 16, 2026
概括
大型语言模型 (LLM) 现在可以通过生成直接行动策略来控制体现的代理. 这种方法通过反和交互数据来完善策略,实现控制任务的最佳解决方案.
科学领域:
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
- 强化学习是一种强化学习.
背景情况:
- 嵌入式代理需要复杂的控制政策来实现现实世界的互动.
- 大型语言模型 (LLM) 具有高级推理能力,但缺乏直接的物理控制机制.
研究的目的:
- 开发一种方法,使LLMs能够生成和完善体内代理的控制政策.
- 为了弥合LLMs的象征性推理和代理控制的亚象征性感官运动数据之间的差距.
主要方法:
- 劳动力管理系统 (LLM) 基于对代理人,环境和目标的文字描述,生成初始控制策略.
- 控制策略的代改进,使用性能反和来自代理相互作用的感觉运动数据.
- 验证经典的控制任务 (体育馆) 和基于物理的任务 (MuJoCo反向摆).
主要成果:
- 拟议的方法有效地使LLM能够控制体现剂.
- 通过像GPT-oss:120b和Qwen2.5:72b这样的紧型LLM成功应用.
- 通过将符号推理与实时交互数据集成来实现最佳或接近最佳的解决方案.
结论:
- 基于LLM的控制策略为嵌入式代理导航和任务执行提供了一个有希望的方法.
- 代改进过程提高了政策的稳定性和适应性.
- 这种方法有助于将LLM的一般智能与物理世界的互动进行整合.
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