Unlocking underrepresented use-cases for large language model-driven human-robot task planning

IF 1.4 4区计算机科学 Q4 ROBOTICS

Advanced Robotics Pub Date : 2024-07-04 DOI:10.1080/01691864.2024.2366974

Selma Wanna, Fabian Parra, Robert Valner, Karl Kruusamäe, Mitch Pryor

引用次数: 0

Abstract

Large language models (LLM) are now the de facto task planners for Embodied AI (EAI) systems. This shift can be attributed to LLMs' powerful, emergent properties which enable their adaptation to do...

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为大型语言模型驱动的人机任务规划揭示代表性不足的用例

大型语言模型（LLM）现在是嵌入式人工智能（EAI）系统事实上的任务规划器。这种转变可归因于 LLM 强大的新兴特性，这些特性使其能够适应不同的任务规划。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Advanced Robotics 工程技术-机器人学

CiteScore

4.10

自引率

20.00%

发文量

102

审稿时长

5.3 months

期刊介绍： Advanced Robotics (AR) is the international journal of the Robotics Society of Japan and has a history of more than twenty years. It is an interdisciplinary journal which integrates publication of all aspects of research on robotics science and technology. Advanced Robotics publishes original research papers and survey papers from all over the world. Issues contain papers on analysis, theory, design, development, implementation and use of robots and robot technology. The journal covers both fundamental robotics and robotics related to applied fields such as service robotics, field robotics, medical robotics, rescue robotics, space robotics, underwater robotics, agriculture robotics, industrial robotics, and robots in emerging fields. It also covers aspects of social and managerial analysis and policy regarding robots. Advanced Robotics (AR) is an international, ranked, peer-reviewed journal which publishes original research contributions to scientific knowledge. All manuscript submissions are subject to initial appraisal by the Editor, and, if found suitable for further consideration, to peer review by independent, anonymous expert referees.