# Learning a natural-language to LTL executable semantic parser for grounded robotics

arXiv: https://arxiv.org/abs/2008.03277 · HTML: https://mlsanity.com/paper/2008.03277/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2020.

## Built on by (most cited first, 22 of 22)

- [LTL2Action: Generalizing LTL Instructions for Multi-Task RL](https://mlsanity.com/paper/2102.06858/) (2021)
- [nl2spec: Interactively Translating Unstructured Natural Language to Temporal Logics with Large Language Models](https://mlsanity.com/paper/2303.04864/) (2023)
- [NL2TL: Transforming Natural Languages to Temporal Logics using Large Language Models](https://mlsanity.com/paper/2305.07766/) (2023)
- [Grounding Complex Natural Language Commands for Temporal Tasks in Unseen Environments](https://mlsanity.com/paper/2302.11649/) (2023)
- [Data-Efficient Learning of Natural Language to Linear Temporal Logic Translators for Robot Task Specification](https://mlsanity.com/paper/2303.08006/) (2023)
- [Formal Specifications from Natural Language](https://mlsanity.com/paper/2206.01962/) (2022)
- [A Survey of Robotic Language Grounding: Tradeoffs between Symbols and Embeddings](https://mlsanity.com/paper/2405.13245/) (2024)
- [SELP: Generating Safe and Efficient Task Plans for Robot Agents with Large Language Models](https://mlsanity.com/paper/2409.19471/) (2024)
- [Verifiably Following Complex Robot Instructions with Foundation Models](https://mlsanity.com/paper/2402.11498/) (2024)
- [Learning to Follow Instructions in Text-Based Games](https://mlsanity.com/paper/2211.04591/) (2022)
- [Learning Neuro-symbolic Programs for Language Guided Robot Manipulation](https://mlsanity.com/paper/2211.06652/) (2022)
- [Grammar-Forced Translation of Natural Language to Temporal Logic using LLMs](https://mlsanity.com/paper/2512.16814/) (2025)
- [NatSGD: A Dataset with Speech, Gestures, and Demonstrations for Robot Learning in Natural Human-Robot Interaction](https://mlsanity.com/paper/2403.02274/) (2024)
- [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)
- [Formal Methods Meet LLMs: Auditing, Monitoring, and Intervention for Compliance of Advanced AI Systems](https://mlsanity.com/paper/2605.16198/) (2026)
- [Structural-Ambiguity-Aware Translation from Natural Language to Signal Temporal Logic](https://mlsanity.com/paper/2603.28426/) (2026)
- [SCP-NL2TL: Selective Conformal Prediction with Semantic Verification for Natural Language to Temporal Logic Specifications](https://mlsanity.com/paper/2608.05439/) (2026)
- [Nl2Hltl2Plan: Scaling Up Natural Language Understanding for Multi-Robots Through Hierarchical Temporal Logic Task Representation](https://mlsanity.com/paper/2408.08188/) (2024)
- [NatSGLD: A Dataset with Speech, Gesture, Logic, and Demonstration for Robot Learning in Natural Human-Robot Interaction](https://mlsanity.com/paper/2502.16718/) (2025)
- [GinSign: Grounding Natural Language Into System Signatures for Temporal Logic Translation](https://mlsanity.com/paper/2512.16770/) (2025)
- [NeuroNL2LTL: A Neurosymbolic Framework for Natural Language Translation of Linear Temporal Logic](https://mlsanity.com/paper/2605.22874/) (2026)

## Abstract (arXiv)

Children acquire their native language with apparent ease by observing how language is used in context and attempting to use it themselves. They do so without laborious annotations, negative examples, or even direct corrections. We take a step toward robots that can do the same by training a grounded semantic parser, which discovers latent linguistic representations that can be used for the execution of natural-language commands. In particular, we focus on the difficult domain of commands with a temporal aspect, whose semantics we capture with Linear Temporal Logic, LTL. Our parser is trained with pairs of sentences and executions as well as an executor. At training time, the parser hypothesizes a meaning representation for the input as a formula in LTL. Three competing pressures allow the parser to discover meaning from language. First, any hypothesized meaning for a sentence must be permissive enough to reflect all the annotated execution trajectories. Second, the executor -- a pretrained end-to-end LTL planner -- must find that the observe trajectories are likely executions of the meaning. Finally, a generator, which reconstructs the original input, encourages the model to find representations that conserve knowledge about the command. Together these ensure that the meaning is neither too general nor too specific. Our model generalizes well, being able to parse and execute both machine-generated and human-generated commands, with near-equal accuracy, despite the fact that the human-generated sentences are much more varied and complex with an open lexicon. The approach presented here is not specific to LTL: it can be applied to any domain where sentence meanings can be hypothesized and an executor can verify these meanings, thus opening the door to many applications for robotic agents.