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

• URL: http://arxiv.org/abs/2603.08814v1
• Date: Mon, 09 Mar 2026 18:13:18 GMT
• Title: Scale-Plan: Scalable Language-Enabled Task Planning for Heterogeneous Multi-Robot Teams
• Abstract summary: Long-horizon task planning for heterogeneous multi-robot systems is essential for deploying collaborative teams in real-world environments.<n>We present Scale-Plan, a scalable LLM-assisted framework that generates compact, task-relevant problem representations from natural language instructions.<n>By filtering irrelevant information prior to planning, Scale-Plan enables efficient decomposition, allocation, and long-horizon plan generation.
• Score: 11.964765256346857
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• PlanningBench: Generating Scalable and Verifiable Planning Data for Evaluating and Training Large Language Models [52.48858778580074]
  Planning is a fundamental capability for large language models (LLMs)<n>PlanningBench is a framework for generating scalable, diverse, verifiable planning data for both evaluation and training.
  arXiv  Detail & Related papers  (2026-05-20T08:10:15Z)
• From Coarse to Fine: Self-Adaptive Hierarchical Planning for LLM Agents [24.79854509473032]
  Large language model-based agents have emerged as powerful approaches for solving dynamic and multi-step tasks.<n>We propose textbfAdaPlan-H, a self-adaptive hierarchical planning mechanism that mimics human planning strategies.<n>Our method significantly improves task execution success rates while mitigating overplanning at the planning level.
  arXiv  Detail & Related papers  (2026-04-25T07:54:23Z)
• DUPLEX: Agentic Dual-System Planning via LLM-Driven Information Extraction [11.920443665332629]
  Large Language Models (LLMs) provide semantic flexibility for robotic task planning.<n>Their susceptibility to hallucination and logical inconsistency limits their reliability in long-horizon domains.<n>We propose a dual-system neuro-symbolic architecture that strictly confines the LLM to schema-guided information extraction.
  arXiv  Detail & Related papers  (2026-03-25T03:57:44Z)
• KGLAMP: Knowledge Graph-guided Language model for Adaptive Multi-robot Planning and Replanning [5.713977302944038]
  We introduce KGLAMP, a knowledge-graph-guided LLM planning framework for heterogeneous multi-robot teams.<n>The framework maintains a structured knowledge graph encoding object relations, spatial reachability, and robot capabilities.<n>Experiments on the MAT-THOR benchmark show that KGLAMP improves performance by at least 25.5% over both LLM-only and PDDL-based variants.
  arXiv  Detail & Related papers  (2026-02-04T01:46:02Z)
• PLAN-TUNING: Post-Training Language Models to Learn Step-by-Step Planning for Complex Problem Solving [66.42260489147617]
  We introduce PLAN-TUNING, a framework that distills synthetic task decompositions from large-scale language models.<n>Plan-TUNING fine-tunes smaller models via supervised and reinforcement-learning objectives to improve complex reasoning.<n>Our analysis demonstrates how planning trajectories improves complex reasoning capabilities.
  arXiv  Detail & Related papers  (2025-07-10T07:30:44Z)
• Interactive and Expressive Code-Augmented Planning with Large Language Models [62.799579304821826]
  Large Language Models (LLMs) demonstrate strong abilities in common-sense reasoning and interactive decision-making. Recent techniques have sought to structure LLM outputs using control flow and other code-adjacent techniques to improve planning performance. We propose REPL-Plan, an LLM planning approach that is fully code-expressive and dynamic.
  arXiv  Detail & Related papers  (2024-11-21T04:23:17Z)
• ET-Plan-Bench: Embodied Task-level Planning Benchmark Towards Spatial-Temporal Cognition with Foundation Models [38.89166693142495]
  ET-Plan-Bench is a benchmark for embodied task planning using Large Language Models (LLMs)<n>It features a controllable and diverse set of embodied tasks varying in different levels of difficulties and complexities.<n>Our benchmark distinguishes itself as a large-scale, quantifiable, highly automated, and fine-grained diagnostic framework.
  arXiv  Detail & Related papers  (2024-10-02T19:56:38Z)
• Unlocking Reasoning Potential in Large Langauge Models by Scaling Code-form Planning [94.76546523689113]
  We introduce CodePlan, a framework that generates and follows textcode-form plans -- pseudocode that outlines high-level, structured reasoning processes. CodePlan effectively captures the rich semantics and control flows inherent to sophisticated reasoning tasks. It achieves a 25.1% relative improvement compared with directly generating responses.
  arXiv  Detail & Related papers  (2024-09-19T04:13:58Z)
• Nl2Hltl2Plan: Scaling Up Natural Language Understanding for Multi-Robots Through Hierarchical Temporal Logic Task Representation [8.180994118420053]
  Nl2Hltl2Plan is a framework that translates natural language commands into hierarchical Linear Temporal Logic (LTL)<n>First, an LLM transforms instructions into a Hierarchical Task Tree, capturing logical and temporal relations.<n>Next, a fine-tuned LLM converts sub-tasks into flat formulas, which are aggregated into hierarchical specifications.
  arXiv  Detail & Related papers  (2024-08-15T14:46:13Z)
• Learning adaptive planning representations with natural language guidance [90.24449752926866]
  This paper describes Ada, a framework for automatically constructing task-specific planning representations. Ada interactively learns a library of planner-compatible high-level action abstractions and low-level controllers adapted to a particular domain of planning tasks.
  arXiv  Detail & Related papers  (2023-12-13T23:35:31Z)
• Embodied Task Planning with Large Language Models [86.63533340293361]
  We propose a TAsk Planing Agent (TaPA) in embodied tasks for grounded planning with physical scene constraint. During inference, we discover the objects in the scene by extending open-vocabulary object detectors to multi-view RGB images collected in different achievable locations. Experimental results show that the generated plan from our TaPA framework can achieve higher success rate than LLaVA and GPT-3.5 by a sizable margin.
  arXiv  Detail & Related papers  (2023-07-04T17:58:25Z)
• AdaPlanner: Adaptive Planning from Feedback with Language Models [56.367020818139665]
  Large language models (LLMs) have recently demonstrated the potential in acting as autonomous agents for sequential decision-making tasks. We propose a closed-loop approach, AdaPlanner, which allows the LLM agent to refine its self-generated plan adaptively in response to environmental feedback. To mitigate hallucination, we develop a code-style LLM prompt structure that facilitates plan generation across a variety of tasks, environments, and agent capabilities.
  arXiv  Detail & Related papers  (2023-05-26T05:52:27Z)
• A Framework for Neurosymbolic Robot Action Planning using Large Language Models [3.0501524254444767]
  We present a framework aimed at bridging the gap between symbolic task planning and machine learning approaches. The rationale is training Large Language Models (LLMs) into a neurosymbolic task planner compatible with the Planning Domain Definition Language (PDDL) Preliminary results in selected domains show that our method can: (i) solve 95.5% of problems in a test data set of 1,000 samples; (ii) produce plans up to 13.5% shorter than a traditional symbolic planner; (iii) reduce average overall waiting times for a plan availability by up to 61.4%.
  arXiv  Detail & Related papers  (2023-03-01T11:54:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.