# Combining Large Language Models and Symbolic Reasoning for Multi-Robot Temporal Planning through Explainable Knowledge Bases

arXiv: https://arxiv.org/abs/2502.19135 · HTML: https://mlsanity.com/paper/2502.19135/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 9 of its 98 references that are in the map.
- First posted on arXiv 26 Feb 2025.

## Placed in

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

## Builds on (most cited first, 9 of 9)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Mixtral of Experts](https://mlsanity.com/paper/2401.04088/) (2024)
- [Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners](https://mlsanity.com/paper/2307.01928/) (2023)
- [REFLECT: Summarizing Robot Experiences for Failure Explanation and Correction](https://mlsanity.com/paper/2306.15724/) (2023)
- [Open-vocabulary Queryable Scene Representations for Real World Planning](https://mlsanity.com/paper/2209.09874/) (2022)
- [Scaling Up and Distilling Down: Language-Guided Robot Skill Acquisition](https://mlsanity.com/paper/2307.14535/) (2023)
- [Task and Motion Planning with Large Language Models for Object Rearrangement](https://mlsanity.com/paper/2303.06247/) (2023)
- [HDDL 2.1: Towards Defining a Formalism and a Semantics for Temporal HTN Planning](https://mlsanity.com/paper/2306.07353/) (2023)
- [Constructing Behavior Trees from Temporal Plans for Robotic Applications](https://mlsanity.com/paper/2406.17379/) (2024)

## Built on by (most cited first, 2 of 2)

- [Rollout Cards: A Reproducibility Standard for Agent Research](https://mlsanity.com/paper/2605.12131/) (2026)
- [VLMs Can Describe, But Not Measure: Object-Centric Scene Understanding for Robotic Manipulation](https://mlsanity.com/paper/2609.28184/) (2026)

## Abstract (arXiv)

We present PLANTOR, a framework for generating and executing multi-robot task plans from natural-language task descriptions through LLM-assisted knowledge-base construction. The approach uses large language models to synthesize a structured Prolog knowledge-base, applies consistency checks to detect and repair modeling errors, generates a high-level symbolic plan, refines it into low-level robot actions, and computes a temporally optimized schedule that is converted into an executable behavior tree. The framework is designed to preserve inspectability by exposing the generated knowledge-base, intermediate plans, and scheduling constraints. We evaluate the approach on scenarios inspired by the Blocks World and Grippers benchmark across multiple language models, and we report both the quality of generated knowledge-bases and the runtime of the planning pipeline. We further demonstrate end-to-end execution in a real multi-arm assembly scenario. The results show that LLM-generated knowledge-bases can substantially reduce manual modeling effort, but may still require human correction. Overall, the paper argues for a hybrid workflow in which language models are used to produce structured symbolic artifacts, while correctness-critical planning and scheduling remain symbolic and inspectable.