让大模型机器人在野外复杂环境自主执行数公里任务
Deploying Foundation Model-Enabled Air and Ground Robots in the Field: Challenges and Opportunities
- 用大语言模型实现机器人在未知环境中的主动探索与规划
- 首次展示数公里级非结构化环境中基于语言指令的无人机自主规划
- 模型轻量化使小模型可在资源受限设备上运行，适合野外部署
将基础模型（FMs）融入机器人系统，使机器人能够理解自然语言并推理环境语义。然而，现有FM驱动的机器人主要在封闭世界中运行，依赖完整先验地图或视野。本文探讨了在野外大规模、非结构化环境中部署FM机器人所面临的挑战与机遇。为有效完成任务，机器人需主动探索、穿越障碍物密集地形、应对意外传感器输入，并在计算资源受限条件下运行。我们介绍了SPINE——一个基于大语言模型的自主框架——在真实野外场景中的多次部署。据我们所知，这是首次在非结构化环境中实现数公里级的大型语言模型驱动机器人规划。SPINE不依赖特定语言模型，支持将大模型蒸馏为可部署于尺寸、重量和功耗（SWaP）受限平台的小模型。通过初步蒸馏工作，我们首次展示了基于机载语言模型的语言驱动无人机规划器。最后，我们提出了未来研究的若干有前景方向。
原文摘要 · Abstract (English)
The integration of foundation models (FMs) into robotics has enabled robots to understand natural language and reason about the semantics in their environments. However, existing FM-enabled robots primary operate in closed-world settings, where the robot is given a full prior map or has a full view of its workspace. This paper addresses the deployment of FM-enabled robots in the field, where missions often require a robot to operate in large-scale and unstructured environments. To effectively accomplish these missions, robots must actively explore their environments, navigate obstacle-cluttered terrain, handle unexpected sensor inputs, and operate with compute constraints. We discuss recent deployments of SPINE, our LLM-enabled autonomy framework, in field robotic settings. To the best of our knowledge, we present the first demonstration of large-scale LLM-enabled robot planning in unstructured environments with several kilometers of missions. SPINE is agnostic to a particular LLM, which allows us to distill small language models capable of running onboard size, weight and power (SWaP) limited platforms. Via preliminary model distillation work, we then present the first language-driven UAV planner using on-device language models. We conclude our paper by proposing several promising directions for future research.
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