Ro-SLM: Onboard Small Language Models for Robot Task Planning and Operation Code Generation
For roboticists deploying AI on edge devices with limited compute or unreliable internet, this provides a practical method to achieve near-LLM performance using smaller models.
Ro-SLM enables small language models (SLMs) to perform robot task planning and code generation on resource-constrained platforms by distilling knowledge from large language models (LLMs). On UAV tasks, Ro-SLM improves SLM performance from incapable to approaching that of LLMs.
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.