CARTIER: Cartographic lAnguage Reasoning Targeted at Instruction Execution for Robots

Abstract-- This work explores the capacity of large language models (LLMs) to address problems at the intersection of spatial planning and natural language interfaces for navigation. We focus on following complex instructions that are more akin to natural conversation than traditional explicit procedural directives typically seen in robotics. Unlike most prior work where navigation directives are provided as simple imperative commands (e.g., "go to the fridge"), we examine implicit directives obtained through conversational interactions.We leverage the 3D simulator AI2Thor to create household query scenarios at scale, and augment it by adding complex language queries for 40 object types. We demonstrate that a robot using our method CARTIER (Cartographic lAnguage Reasoning Targeted at Instruction Execution for Robots) can parse descriptive language queries up to 42% more reliably than existing LLM-enabled methods by exploiting the ability of LLMs to interpret the user interaction in the context of the objects in the scenario. This paper explores the extent to which natural interaction is possible between human and robot in the context of a navigation task. We seek to answer the question: "Can a robot infer its task in a navigational context without Figure 1: CARTIER prompts an LLM with knowledge about receiving an explicit command?" Household robotic tasks are a robot's environment in order to parse user intent from often formulated using imperative commands with a template implicit, conversational queries. It then informs the robot structure that can be abstracted as "go-do" commands (go where to navigate in order to help the user.