基于llm的仿人机器人复杂行为学习。

IF 27.5 1区计算机科学 Q1 ROBOTICS

Science Robotics Pub Date : 2025-01-22 DOI:10.1126/scirobotics.adv4627

Amos Matsiko

引用次数: 0

摘要

人形机器人的复杂行为学习可以通过大型语言模型辅助的自然交互来实现。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Humanoid robot learning of complex behaviors with LLMs

Learning complex behaviors by humanoid robots could be achieved with natural interactions aided by large language models.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Science Robotics Mathematics-Control and Optimization

CiteScore

30.60

自引率

2.80%

发文量

期刊介绍： Science Robotics publishes original, peer-reviewed, science- or engineering-based research articles that advance the field of robotics. The journal also features editor-commissioned Reviews. An international team of academic editors holds Science Robotics articles to the same high-quality standard that is the hallmark of the Science family of journals. Sub-topics include: actuators, advanced materials, artificial Intelligence, autonomous vehicles, bio-inspired design, exoskeletons, fabrication, field robotics, human-robot interaction, humanoids, industrial robotics, kinematics, machine learning, material science, medical technology, motion planning and control, micro- and nano-robotics, multi-robot control, sensors, service robotics, social and ethical issues, soft robotics, and space, planetary and undersea exploration.