# Scale-Plan: Scalable Language-Enabled Task Planning for Heterogeneous Multi-Robot Teams

arXiv: https://arxiv.org/abs/2603.08814 · HTML: https://mlsanity.com/paper/2603.08814/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 29 references that are in the map.
- First posted on arXiv 9 Mar 2026.

## Builds on (most cited first, 18 of 18)

- [AI2-THOR: An Interactive 3D Environment for Visual AI](https://mlsanity.com/paper/1712.05474/) (2017)
- [LLM+P: Empowering Large Language Models with Optimal Planning Proficiency](https://mlsanity.com/paper/2304.11477/) (2023)
- [LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models](https://mlsanity.com/paper/2212.04088/) (2022)
- [SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning](https://mlsanity.com/paper/2307.06135/) (2023)
- [3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera](https://mlsanity.com/paper/1910.02527/) (2019)
- [Text2Motion: From Natural Language Instructions to Feasible Plans](https://mlsanity.com/paper/2303.12153/) (2023)
- [SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models](https://mlsanity.com/paper/2309.10062/) (2023)
- [SDRL: Interpretable and Data-efficient Deep Reinforcement Learning Leveraging Symbolic Planning](https://mlsanity.com/paper/1811.00090/) (2018)
- [TASKOGRAPHY: Evaluating robot task planning over large 3D scene graphs](https://mlsanity.com/paper/2207.05006/) (2022)
- [COHERENT: Collaboration of Heterogeneous Multi-Robot System with Large Language Models](https://mlsanity.com/paper/2409.15146/) (2024)
- [Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks](https://mlsanity.com/paper/2009.05613/) (2020)
- [LaMMA-P: Generalizable Multi-Agent Long-Horizon Task Allocation and Planning with LM-Driven PDDL Planner](https://mlsanity.com/paper/2409.20560/) (2024)
- [GFlowVLM: Enhancing Multi-step Reasoning in Vision-Language Models with Generative Flow Networks](https://mlsanity.com/paper/2503.06514/) (2025)
- [Learning STRIPS Action Models with Classical Planning](https://mlsanity.com/paper/1903.01153/) (2019)
- [Generalized Mission Planning for Heterogeneous Multi-Robot Teams via LLM-constructed Hierarchical Trees](https://mlsanity.com/paper/2501.16539/) (2025)
- [Graph-Grounded LLMs: Leveraging Graphical Function Calling to Minimize LLM Hallucinations](https://mlsanity.com/paper/2503.10941/) (2025)
- [Incentivizing Collaboration in Heterogeneous Teams via Common-Pool Resource Games](https://mlsanity.com/paper/1908.03938/) (2019)
- [KGLAMP: Knowledge Graph-guided Language model for Adaptive Multi-robot Planning and Replanning](https://mlsanity.com/paper/2602.04129/) (2026)

## Built on by (most cited first, 1 of 1)

- [Drive My Way: Preference Alignment of Vision-Language-Action Model for Personalized Driving](https://mlsanity.com/paper/2603.25740/) (2026)

## Abstract (arXiv)

Long-horizon task planning for heterogeneous multi-robot systems is essential for deploying collaborative teams in real-world environments; yet, it remains challenging due to the large volume of perceptual information, much of which is irrelevant to task objectives and burdens planning. Traditional symbolic planners rely on manually constructed problem specifications, limiting scalability and adaptability, while recent large language model (LLM)-based approaches often suffer from hallucinations and weak grounding-i.e., poor alignment between generated plans and actual environmental objects and constraints-in object-rich settings. We present Scale-Plan, a scalable LLM-assisted framework that generates compact, task-relevant problem representations from natural language instructions. Given a PDDL domain specification, Scale-Plan constructs an action graph capturing domain structure and uses shallow LLM reasoning to guide a structured graph search that identifies a minimal subset of relevant actions and objects. By filtering irrelevant information prior to planning, Scale-Plan enables efficient decomposition, allocation, and long-horizon plan generation. We evaluate our approach on complex multi-agent tasks and introduce MAT2-THOR, a cleaned benchmark built on AI2-THOR for reliable evaluation of multi-robot planning systems. Scale-Plan outperforms pure LLM and hybrid LLM-PDDL baselines across all metrics, improving scalability and reliability. Project website: https://github.com/honda-research-institute/Scale_Plan