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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...

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Updated: May 22, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

嵌入式大型语言模型使机器人能够在不可预测的环境中完成复杂的任务.

Ruaridh Mon-Williams^1,2,3, Gen Li¹, Ran Long¹

¹University of Edinburgh, Edinburgh, UK.

Nature machine intelligence

|May 20, 2025

概括

此摘要是机器生成的。

机器人现在可以在不可预测的环境中完成复杂的,长时间的任务,使用新的嵌入式大型语言模型启用机器人 (ELLMER) 框架. 这种由人工智能驱动的系统将人工智能与机器人传感运动技能相结合,以适应性完成任务.

关键词:

计算机科学计算机科学工程工程师工程师工程师

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科学领域:

机器人和人工智能机器人和人工智能
机器学习机器学习
具体的人工智能 (AI)

背景情况:

在不可预测的环境中完成复杂的任务挑战了当前的机器人系统,需要机器智能的进步.
感官运动能力对人类智能至关重要,这表明它们对生物灵感机器智能的重要性.
将人工智能 (AI) 与机器人传感动力能力相结合,为开发先进机器人提供了一个有前途的方向.

研究的目的:

引入一个嵌入式大型语言模型启用机器人 (ELLMER) 框架,用于在不可预测的环境中完成复杂,长时间的任务.
通过将力量和视觉反纳入行动计划,使机器人能够适应不断变化的条件.
展示人工智能驱动机器人的能力,以执行复杂的任务,需要连续的子任务和多种反机制.

主要方法:

在ELLMER框架内使用了GPT-4和检索增强生成基础设施.
从知识库中提取上下文相关的例子,以生成行动计划.
集成的力量和视觉反用于适应性控制和任务执行.

主要成果:

埃尔默框架成功地使机器人能够完成复杂的任务,包括咖啡制作和盘子装饰.
展示了机器人执行一系列子任务的能力,例如打开抽和倒,有明显的反.
展示了框架在执行任务时适应不断变化的环境条件的有效性.

结论:

埃尔默框架代表了创造可扩展,高效和智能机器人的重大进展.
这种方法使机器人能够在不确定的和动态的环境中执行复杂的任务.
大型语言模型和感官运动反的集成是体内人工智能的关键进步.