Grounding Complex Natural Language Commands for Temporal Tasks in Unseen Environments
This addresses the challenge of enabling robots to understand and execute complex temporal commands in new environments without environment-specific data, representing a significant advance over existing methods.
The paper tackles the problem of grounding natural language navigational commands to linear temporal logic (LTL) specifications in unseen environments without prior training data, achieving state-of-the-art performance by grounding commands in 21 city-scaled environments and enabling a physical robot to follow 52 diverse commands in indoor settings.
Grounding navigational commands to linear temporal logic (LTL) leverages its unambiguous semantics for reasoning about long-horizon tasks and verifying the satisfaction of temporal constraints. Existing approaches require training data from the specific environment and landmarks that will be used in natural language to understand commands in those environments. We propose Lang2LTL, a modular system and a software package that leverages large language models (LLMs) to ground temporal navigational commands to LTL specifications in environments without prior language data. We comprehensively evaluate Lang2LTL for five well-defined generalization behaviors. Lang2LTL demonstrates the state-of-the-art ability of a single model to ground navigational commands to diverse temporal specifications in 21 city-scaled environments. Finally, we demonstrate a physical robot using Lang2LTL can follow 52 semantically diverse navigational commands in two indoor environments.