# Generalizing to New Domains by Mapping Natural Language to Lifted LTL

arXiv: https://arxiv.org/abs/2110.05603 · HTML: https://mlsanity.com/paper/2110.05603/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 11 Oct 2021.

## Builds on (most cited first, 5 of 5)

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Incorporating Copying Mechanism in Sequence-to-Sequence Learning](https://mlsanity.com/paper/1603.06393/) (2016)
- [Probably Approximately Correct MDP Learning and Control With Temporal Logic Constraints](https://mlsanity.com/paper/1404.7073/) (2014)
- [A Learning Based Approach to Control Synthesis of Markov Decision Processes for Linear Temporal Logic Specifications](https://mlsanity.com/paper/1409.5486/) (2014)
- [Planning with State Abstractions for Non-Markovian Task Specifications](https://mlsanity.com/paper/1905.12096/) (2019)

## Built on by (most cited first, 11 of 11)

- [NL2TL: Transforming Natural Languages to Temporal Logics using Large Language Models](https://mlsanity.com/paper/2305.07766/) (2023)
- [Data-Efficient Learning of Natural Language to Linear Temporal Logic Translators for Robot Task Specification](https://mlsanity.com/paper/2303.08006/) (2023)
- [Agents4PLC: Automating Closed-loop PLC Code Generation and Verification in Industrial Control Systems using LLM-based Agents](https://mlsanity.com/paper/2410.14209/) (2024)
- [A Survey of Robotic Language Grounding: Tradeoffs between Symbols and Embeddings](https://mlsanity.com/paper/2405.13245/) (2024)
- [Verifiably Following Complex Robot Instructions with Foundation Models](https://mlsanity.com/paper/2402.11498/) (2024)
- [VerifyLLM: LLM-Based Pre-Execution Task Plan Verification for Robots](https://mlsanity.com/paper/2507.05118/) (2025)
- [Grammar-Forced Translation of Natural Language to Temporal Logic using LLMs](https://mlsanity.com/paper/2512.16814/) (2025)
- [Verifiable Natural Language to Linear Temporal Logic Translation: A Benchmark Dataset and Evaluation Suite](https://mlsanity.com/paper/2507.00877/) (2025)
- [CoT-TL: Low-Resource Temporal Knowledge Representation of Planning Instructions Using Chain-of-Thought Reasoning](https://mlsanity.com/paper/2410.16207/) (2024)
- [A Concept-Centric Approach to Multi-Modality Learning](https://mlsanity.com/paper/2412.13847/) (2024)
- [GinSign: Grounding Natural Language Into System Signatures for Temporal Logic Translation](https://mlsanity.com/paper/2512.16770/) (2025)

## Abstract (arXiv)

Recent work on using natural language to specify commands to robots has grounded that language to LTL. However, mapping natural language task specifications to LTL task specifications using language models require probability distributions over finite vocabulary. Existing state-of-the-art methods have extended this finite vocabulary to include unseen terms from the input sequence to improve output generalization. However, novel out-of-vocabulary atomic propositions cannot be generated using these methods. To overcome this, we introduce an intermediate contextual query representation which can be learned from single positive task specification examples, associating a contextual query with an LTL template. We demonstrate that this intermediate representation allows for generalization over unseen object references, assuming accurate groundings are available. We compare our method of mapping natural language task specifications to intermediate contextual queries against state-of-the-art CopyNet models capable of translating natural language to LTL, by evaluating whether correct LTL for manipulation and navigation task specifications can be output, and show that our method outperforms the CopyNet model on unseen object references. We demonstrate that the grounded LTL our method outputs can be used for planning in a simulated OO-MDP environment. Finally, we discuss some common failure modes encountered when translating natural language task specifications to grounded LTL.