用大模型教小模型做路径规划，效率高还适合部署在边缘设备。
SmallPlan: Leverage Small Language Models for Sequential Path Planning with Simulation-Powered, LLM-Guided Distillation
- 用大模型作老师，通过模拟与强化学习训练轻量小模型。
- 小模型在复杂场景中导航成功率接近GPT-4o，且无幻觉问题。
- 适合资源受限的机器人系统，提升实际部署可行性。
在大规模复杂环境中实现高效路径规划仍是机器人领域的难题。尽管大型语言模型（LLMs）具备强大的推理能力，但其高计算开销和低适应性限制了在边缘设备上的实时部署。我们提出SmallPlan——一种利用LLM作为教师模型，训练轻量级小语言模型（SLMs）进行高层路径规划的新框架。在SmallPlan中，SLMs生成最优动作序列，以在紧凑表示全尺度3D场景的场景图中导航。通过模拟驱动、交错进行的监督微调（SFT）与强化学习（RL），SLMs不仅成功完成导航任务，还学会关注距离等关键因素，实现更高效的路径规划。实验表明，经微调的SLMs在顺序路径规划上表现媲美GPT-4o，且未出现幻觉或过拟合现象。SmallPlan资源消耗低，非常适合边缘设备部署，推动自主机器人技术的实际应用。代码已开源：https://github.com/quangpham2006/SmallPlan
原文摘要 · Abstract (English)
Efficient path planning in robotics, particularly within large-scale, complex environments, remains a significant hurdle. While Large Language Models (LLMs) offer strong reasoning capabilities, their high computational cost and limited adaptability hinder real-time deployment on edge devices. We present SmallPlan - a novel framework leveraging LLMs as teacher models to train lightweight Small Language Models (SLMs) for high-level path planning tasks. In SmallPlan, the SLMs provide optimal action sequences to navigate across scene graphs that compactly represent full-scaled 3D scenes. The SLMs are trained in a simulation-powered, interleaved manner with LLM-guided supervised fine-tuning (SFT) and reinforcement learning (RL). This strategy not only enables SLMs to successfully complete navigation tasks but also makes them aware of important factors like distance travel, providing more efficient path planning. Through experiments, we demonstrate that the fine-tuned SLMs perform competitively with larger models like GPT-4o on sequential path planning, without suffering from hallucination and overfitting. SmallPlan is resource-efficient, making it well-suited for edge-device deployment and advancing practical autonomous robotics. Our source code is available here: https://github.com/quangpham2006/SmallPlan
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