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.
Powered by A.I., New System Makes Human-to-Robot Communication More Seamless [Brown News November 6th, 2023]
@inproceedings{liu2023lang2ltl,
title = {Grounding Complex Natural Language Commands for Temporal Tasks in Unseen Environments},
author = {Liu, Jason Xinyu and Yang, Ziyi and Idrees, Ifrah and Liang, Sam and Schornstein, Benjamin and Tellex, Stefanie and Shah, Ankit},
booktitle = {Conference on Robot Learning (CoRL)},
url = {https://arxiv.org/abs/2302.11649}
year = {2023},
}