# Ask, Reason, Assist: Robot Collaboration via Natural Language and Temporal Logic

arXiv: https://arxiv.org/abs/2509.23506 · HTML: https://mlsanity.com/paper/2509.23506/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 27 Sep 2025.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)

## Builds on (most cited first, 6 of 6)

- [Guiding LLMs The Right Way: Fast, Non-Invasive Constrained Generation](https://mlsanity.com/paper/2403.06988/) (2024)
- [Real-Time Anomaly Detection and Reactive Planning with Large Language Models](https://mlsanity.com/paper/2407.08735/) (2024)
- [NL2TL: Transforming Natural Languages to Temporal Logics using Large Language Models](https://mlsanity.com/paper/2305.07766/) (2023)
- [EMOS: Embodiment-aware Heterogeneous Multi-robot Operating System with LLM Agents](https://mlsanity.com/paper/2410.22662/) (2024)
- [ConformalNL2LTL: Translating Natural Language Instructions into Temporal Logic Formulas with Conformal Correctness Guarantees](https://mlsanity.com/paper/2504.21022/) (2025)
- [Enhancing Transformation from Natural Language to Signal Temporal Logic Using LLMs with Diverse External Knowledge](https://mlsanity.com/paper/2505.20658/) (2025)

## Abstract (arXiv)

Increased robot deployment, such as in warehousing, has revealed a need for collaboration among heterogeneous robot teams to resolve unforeseen conflicts. To this end, we propose a peer-to-peer coordination protocol that enables robots to request and provide help without a central task allocator. The process begins when a robot detects a conflict and uses a Large Language Model (LLM) to decide whether external assistance is required. If so, it crafts and broadcasts a natural language (NL) help request. Potential helper robots reason over the request and respond with offers of assistance, including information about the effect on their ongoing tasks. Helper reasoning is implemented via an LLM grounded in Signal Temporal Logic (STL) using a Backus-Naur Form (BNF) grammar, ensuring syntactically valid NL-to-STL translations, which are then solved as a Mixed Integer Linear Program (MILP). Finally, the requester robot selects a helper by reasoning over the expected increase in system-level total task completion time. We evaluated our framework through experiments comparing different helper-selection strategies and found that considering multiple offers allows the requester to minimize added makespan. Our approach significantly outperforms heuristics such as selecting the nearest available candidate helper robot, and achieves performance comparable to a centralized "Oracle" baseline but without heavy information demands.