让小型语言模型在机器人上本地运行，实现任务规划与代码生成。
Ro-SLM: Onboard Small Language Models for Robot Task Planning and Operation Code Generation
- 用大模型生成数据并蒸馏知识，训练可本地部署的小模型。
- 在无人机任务中，小模型性能接近大模型，可完成复杂任务规划。
- 适合资源受限的机器人，如无人机、小型车，无需依赖云端。
大型语言模型（LLM）赋予机器人理解人类指令的上下文推理能力。然而，现有基于LLM的机器人通常依赖云端模型或高性能计算设施，在网络不稳定或计算资源受限的场景（如无人机和小型地面车辆）下难以部署。为此，本文提出Ro-SLM框架，通过蒸馏LLM的知识与推理能力，实现可本地部署的微调小型语言模型（SLM）。Ro-SLM首先利用LLM自动生成多样化的任务指令，以最少的人工参与生成对应的正确代码，并将指令增强至真实应用场景。随后，使用该数据集对Ro-SLM进行微调，期间以LLM作为奖励函数指导训练。在无人机操作任务上的大量实验表明，经过训练的SLM从无法支持机器人任务规划与代码生成，提升至性能接近原始大模型的表现。
原文摘要 · Abstract (English)
Recent advances in large language models (LLMs) provide robots with contextual reasoning abilities to comprehend human instructions. Yet, current LLM-enabled robots typically depend on cloud-based models or high-performance computing infrastructure, which limit their deployment on robots under unreliable internet environments or with constrained computational resources, such as UAVs and small ground vehicles. Thus, deploying fine-tuned small language models (SLMs) that support onboard deployment offers a promising alternative. This paper introduces Ro-SLM, a framework that enables reliable SLM-driven robot operation by distilling LLMs' knowledge and reasoning. Ro-SLM starts from dataset synthesis by leveraging LLMs to generate diverse task instructions, produce corresponding ground truth code with minimal human assistance, and augment instructions into real-world application scenarios. Ro-SLM is then fine-tuned with the dataset, in which LLM serves as a reward function to guide the training. Extensive experiments on UAV operation tasks demonstrate that Ro-SLM improves the performance of SLM from being incapable of supporting robotic task planning and code generation to achieving performance that approaches LLM.
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