一篇综述论文回顾了大型语言模型（LLM）在多机器人系统（MRS）中的集成，将应用从高级任务分配到低级动作生成进行了分类。它强调了数学推理能力限制和幻觉等挑战，同时也概述了微调和推理技术未来的研究机会。另外，另一篇论文对25个开源语言模型在社交机器人边缘设备的部署进行了基准测试，评估了推理效率、通用知识和教学有效性。 AI
影响 探讨了LLM在机器人技术中的集成，以增强协调性和效率，并对社交机器人边缘部署的模型进行了基准测试。
排序理由 该集群包含两篇学术论文，讨论了语言模型在机器人技术中的应用和基准测试。
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arXiv:2502.03814v5 Announce Type: replace-cross Abstract: The rapid advancement of Large Language Models (LLMs) has opened new possibilities in Multi-Robot Systems (MRS), enabling enhanced communication, task allocation and planning, and human-robot interaction. Unlike traditiona…
arXiv:2605.03111v1 Announce Type: cross Abstract: Social-educational robots designed for socially interactive pedagogical support, such as the Robot Study Companion (RSC), rely on responsive, privacy-preserving interaction despite severely limited compute. However, there is a gap…
Social-educational robots designed for socially interactive pedagogical support, such as the Robot Study Companion (RSC), rely on responsive, privacy-preserving interaction despite severely limited compute. However, there is a gap in systematic benchmarking of language models for…