Lang2LTL: NL-to-LTL Translation Pipeline

• Lang2LTL is a modular system that translates natural language instructions into formal LTL specifications via a three-stage process of parsing, grounding, and synthesis.
• The pipeline leverages advanced LLMs to achieve nearly 98% accuracy in referring expression recognition and grounding, enabling reliable zero-shot generalization.
• It deterministically synthesizes LTL formulas compatible with automata-based planners, offering formal guarantees for navigation, manipulation, and mission execution.

Lang2LTL is a modular pipeline and methodology for translating complex natural language instructions—particularly for navigation and manipulation tasks—into Linear Temporal Logic (LTL) specifications suitable for formal reasoning and planning in unseen environments. The system leverages LLMs to parse, ground, and synthesize temporal logic representations from free-form natural language, supporting zero-shot generalization to new domains. Lang2LTL provides a deterministic interface between human instructions and formal robot control, yielding executable LTL formulas compatible with existing automata-based planners and admitting formal guarantees (Liu et al., 2023).

1. System Architecture and Workflow

Lang2LTL is structured as a three-stage modular pipeline:

1. Referring Expression Recognition (RER): The system parses the input English command, extracting noun phrases that refer to semantic landmarks (e.g., “the store on Main Street," “St. James Church, a Christian place of worship on Harrison Avenue”). This step employs LLMs in an in-context learning configuration, typically GPT-4, achieving high extraction accuracy (98.01% ± 2.08%).
2. Referring Expression Grounding (REG): The recognized referring expressions are embedded via the LLM's text encoder and matched against the semantic metadata of known map landmarks (e.g., OSM tags, addresses, amenity classes) by nearest-neighbor search in semantic space, yielding 98.20% ± 2.30% grounding accuracy across diverse urban environments.
3. LTL Formula Synthesis: Landmark placeholders (A, B, etc.) replace grounded expressions, abstracting the command (“lifted utterance”). This lifted utterance is translated into an LTL formula using models trained specifically for logical composition. A fine-tuned T5-Base model operating under type-constrained decoding produces the final LTL semantic template. The groundings from Stage 2 are then substituted back, yielding a concrete LTL formula.

LTL Grammar and Patterns

Lang2LTL employs standard future-only LTL syntax:

$$\varphi ::= \alpha \mid \neg \varphi \mid \varphi_1 \lor \varphi_2 \mid X\,\varphi \mid \varphi_1\, U\, \varphi_2$$

Atomic propositions ($\alpha$) are placeholders for grounded entities. Complex task structures, such as unordered sequencing (“visit in any order”) map to formulas like $\bigwedge_{i=1}^n F p_i$, ordered patterns to conjunctions of $G$ and $U$ formulas (Liu et al., 2023).

2. Generalization, Robustness, and Evaluation

Lang2LTL was evaluated according to five robust generalization criteria:

1. Utterance Holdout: Robustness to paraphrased instructions
2. Substitution Holdout: Robustness to unseen combinations of propositions
3. Vocabulary Shift Holdout: Use of placeholder symbols not seen during training
4. Formula Skeleton Holdout: Outputting LTL formulae with structural patterns absent during training
5. Type Holdout: Generation of LTL patterns corresponding to novel mission types

Empirically, the system achieved over 98% exact-match accuracy on held-out paraphrase sets. Under more challenging generalization splits, performance degrades gracefully but remains state-of-the-art or better than copy-augmented sequence-to-sequence baselines (e.g., CopyNet).

In zero-shot cross-domain evaluation (on datasets such as Cleanup-World and OSM), Lang2LTL demonstrates 81.83% end-to-end grounding accuracy—surpassing both direct GPT-4 prompts and previous supervised architectures. On 21 unseen urban maps, this constitutes the first successful demonstration of city-scale, zero-shot temporal mission grounding over 15 distinct temporal patterns (Liu et al., 2023).

Component	Accuracy (%)	Benchmark
RER (landmark parsing)	98.01 ± 2.08	5 splits, 21 city-scale envs
REG (grounding)	98.20 ± 2.30	5 splits, 21 city-scale envs
End-to-end (full pipe)	81.83	OSM datasets (21 envs, zero-shot)

3. Comparison With Related Translational Frameworks

Lang2LTL exemplifies a class of neural-symbolic pipelines targeting natural language to LTL translation with particular emphasis on modularity, generalization, and formal correctness. Comparable concurrent and recent frameworks (LTLCodeGen (Rabiei et al., 10 Mar 2025), LTLGuard (Andresel et al., 5 Mar 2026), Req2LTL (Ma et al., 19 Dec 2025), ConformalNL2LTL (Wang et al., 22 Apr 2025)) differ in pipeline structure, guarantees, semantic decomposition strategy, and target applications.

Distinctive aspects of Lang2LTL:

• Explicit separation of parsing, grounding, and synthesis stages
• LLM-based semantic matching for landmarks, enabling robust operation on unseen semantic classes and environments without scene-specific retraining
• Type-constrained decoding during LTL formula synthesis to ensure syntactic correctness and adherence to temporal mission templates
• Empirical validation at both city and indoor robot scale, demonstrating strong compositional and real-world robustness

Contrast with representative systems:

System	Parsing Strategy	Syntactic Guarantee	Formal Correctness	Usage Mode
Lang2LTL	Modular LLM pipeline	Constrained decode	Planner guarantees	Navigation, RL tasks
LTLCodeGen	LLM code generation	Code/test loop	Automaton-based	Robot motion planning
LTLGuard	Masked grammar generation	DFA grammar mask	BLACK consistency	Software, req. checking
Req2LTL	Hierarchical OnionL IR	Rule-based synth	Provable soundness	Aerospace, embedded
ConformalNL2LTL	QA sequence + Conformal	CP sets/semantics	Coverage α	Robust translation

4. Real-World Deployment and Performance

Lang2LTL has been validated in physical robotics, notably with a Boston Dynamics Spot quadruped executing 52 semantically varied commands in novel indoor environments, each mapped to eight semantic waypoints. Of 40 achievable missions, all were correctly grounded and executed. The pipeline also detects unsatisfiable commands—aborting with formal guarantees, a capability absent in end-to-end “Code-as-Policies” baselines (which achieved only 23/52 success).

Qualitative tests highlight Lang2LTL’s capacity to parse and plan for compositionally or temporally complex commands, such as “visit the brown desk exactly three times but avoid the white table after the second visit” and “go to A, then B, or else to C.” The generated LTL specifications, coupled with automata-based planners (e.g., AP-MDP), support robust execution and verifiable mission outcomes (Liu et al., 2023).

5. Limitations and Scalability

Lang2LTL’s performance is constrained primarily by the ambiguity and variability of free-form instruction, LLM context window limits (especially with many object classes/propositions), and challenges in resolving ambiguous or context-dependent semantics not explicit in map data. The pipeline assumes that referring expressions can be reliably grounded using available metadata. Highly contextual or ambiguous commands in novel settings may escape robust parsing or grounding, as observed in competitive ablation and error analyses.

There is no explicit formal correctness guarantee at the NL→LTL mapping stage—formal guarantees are downstream, inheriting from the correctness of LTL planners and automata semantics. Handling of continuous-time or quantitive constraints (as in Signal Temporal Logic) remains outside the current implementation.

6. Extensions, Variants, and Broader Impact

The three-stage parsing–grounding–synthesis abstraction pioneered in Lang2LTL has influenced subsequent architectural advances. LTLCodeGen extends the paradigm to semantic navigation with object classes, employing Python-based code-generation and syntax-feedback loops (Rabiei et al., 10 Mar 2025). LTLGuard incorporates grammar-masked decoding and conflict-checking via symbolic analyzers (Andresel et al., 5 Mar 2026). Req2LTL introduces an intermediate “OnionL” layer for semantic decomposition (Ma et al., 19 Dec 2025). ConformalNL2LTL applies conformal prediction to permit user-tunable correctness rates at the cost of user/interactive “help” (Wang et al., 22 Apr 2025).

Lang2LTL’s methodology is widely applicable beyond robot navigation—for verification of temporal properties in software, formal methods for cyber-physical systems, and dynamic task planning in safety-critical domains. Its data-efficient, zero-shot generalization advances serve as a blueprint for scalable, robust natural-language-to-formal-logic systems in high-assurance domains.