As robots become increasingly capable, users will want to describe high-level missions and have robots infer the relevant details. Because pre-built maps are difficult to obtain in many realistic settings, accomplishing such missions will require the robot to map and plan online. While many semantic planning methods operate online, they are typically designed for well specified missions such as object search or exploration. Recently, Large Language Models (LLMs) have demonstrated powerful contextual reasoning abilities over a range of robotic tasks described in natural language. However, existing LLM-enabled planners typically do not consider online planning or complex missions; rather, relevant subtasks and semantics are provided by a pre-built map or a user. We address these limitations via SPINE, an online planner for missions with incomplete mission specifications provided in natural language. The planner uses an LLM to reason about subtasks implied by the mission specification and then realizes these subtasks in a receding horizon framework. Tasks are automatically validated for safety and refined online with new map observations. We evaluate SPINE in simulation and real-world settings with missions that require multiple steps of semantic reasoning and exploration in cluttered outdoor environments of over 20,000m$^2$. Compared to baselines that use existing LLM-enabled planning approaches, our method is over twice as efficient in terms of time and distance, requires less user interactions, and does not require a full map. Additional resources are provided at https://zacravichandran.github.io/SPINE.

翻译：随着机器人能力日益增强，用户将倾向于描述高层次任务并让机器人推断相关细节。由于在许多现实场景中难以获得预构建地图，完成此类任务需要机器人进行在线建图与规划。尽管许多语义规划方法支持在线运行，但它们通常针对明确指定的任务（如目标搜索或环境探索）而设计。近年来，大语言模型（LLMs）在自然语言描述的各类机器人任务中展现出强大的情境推理能力。然而，现有基于LLM的规划器通常未考虑在线规划或复杂任务；相关子任务与语义信息往往依赖于预构建地图或用户提供。我们通过SPINE解决这些局限性——这是一种针对自然语言描述的不完全任务规约的在线规划器。该规划器利用LLM推理任务规约所隐含的子任务，并在滚动时域框架中实现这些子任务。系统会自动验证任务的安全性，并依据新增地图观测在线优化任务。我们在仿真与真实场景中对SPINE进行了评估，测试任务要求机器人在超过20,000m$^2$的杂乱户外环境中进行多层级语义推理与探索。相较于采用现有LLM规划方法的基线方案，我们的方法在时间与距离效率上提升超过两倍，所需用户交互更少，且无需完整地图。更多资源详见 https://zacravichandran.github.io/SPINE。