一个新的声音在主角批评者强化学习与人类反框架增强机器人导航
Alabhya Sharma1, Ananthakrishnan Balasundaram2, Ayesha Shaik3
1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, India.
Scientific reports
|February 28, 2025
概括
这种新的Voice in Head (ViH) 框架使用大型语言模型 (LLM) 和强化学习来实现先进的机器人导航和交互,在复杂的环境中达到高达94.54%的成功率.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 机器人导航和在复杂环境中的交互仍然具有挑战性.
- 将自然语言理解与机器人控制相结合是一个活跃的研究领域.
- 当前的系统往往缺乏适应性和复杂的推理能力.
研究的目的:
- 为增强机器人导航和交互引入一个新的"头部声音" (ViH) 框架.
- 利用大型语言模型 (LLM) 和语义搜索来实现直观的人机通信.
- 开发一个安全,适应和可扩展的认知机器人系统.
主要方法:
- 在强化学习 (RL) 循环中将GPT和Gemini驱动的LLMs作为Actor和Critic组件集成.
- 利用Azure AI 搜索一个语义搜索机制,使自然语言查询.
- 实施强化学习与人类反 (RLHF) 为了安全和解决LLM的局限性.
主要成果:
- ViH框架实现了高达94.54%的成功率,超过了现有基准.
- 在复杂的环境中展示了有效的机器人导航和交互.
- 该系统被证明是模块化和可扩展的,可以适应各种应用领域.
结论:
- ViH框架代表了认知机器人技术的重大进步.
- 结合LLMs,RL和语义搜索的混合方法增强了自主系统的能力.
- 这项研究有助于开发智能自主系统,并朝着人工通用智能迈进.
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