README.md

Data-Efficient Learning of Natural Language to Linear Temporal Logic Translators for Robot Task Specification

[Homepage] [Paper] [Video] [Poster]

The associated repo for paper "Data-Efficient Learning of Natural Language to Linear Temporal Logic Translators for Robot Task Specification".

Introduction

The dataset is modified based on the following projects, in which we replace the task related descriptions, into task independent descriptions

task related NL2TL example (from Efficient-Eng-2-LTL)

"globally ( and ( until ( scan , not ( any cubes ) ) , finally ( any cubes ) ) )": {
    "formula": "globally ( and ( until ( scan , not ( any cubes ) ) , finally ( any cubes ) ) )",
    "raw": "G & U S ! A F A"
  },

task independent NL2TL example:

{"natural": "go through the P01 until you get to the  P04", "raw_ltl": "F ( P01 A ( F P04 ) )"}

Based task related NL2TL datasets:

• datasets
  • Efficient-Eng-2-LTL
  • Lang2LTL
  • nl2spec
  • NL2TL

Cite

@misc{xu2024scalingnaturallanguageunderstanding,
      title={Scaling Up Natural Language Understanding for Multi-Robots Through the Lens of Hierarchy}, 
      author={Shaojun Xu and Xusheng Luo and Yutong Huang and Letian Leng and Ruixuan Liu and Changliu Liu},
      year={2024},
      eprint={2408.08188},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2408.08188}, 
}