CARTIER: Cartographic lAnguage Reasoning Targeted at Instruction Execution for Robots
This addresses the challenge of natural language interfaces for robotics navigation, though it is incremental as it builds on prior LLM work with specific enhancements.
The paper tackles the problem of enabling robots to follow complex, conversational navigation instructions in household environments, demonstrating that their method CARTIER improves reliability by 42% over existing LLM-enabled methods.
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.