mlsanityData to 14 Oct 2026

Multi-Agent Systems for Robotic Autonomy with LLMs

arXiv 2505.05762 ↗First posted 9 May 2025cs.RO cs.AIcited by 9

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

Since the advent of Large Language Models (LLMs), various research based on such models have maintained significant academic attention and impact, especially in AI and robotics. In this paper, we propose a multi-agent framework with LLMs to construct an integrated system for robotic task analysis, mechanical design, and path generation. The framework includes three core agents: Task Analyst, Robot Designer, and Reinforcement Learning Designer. Outputs are formatted as multimodal results, such as code files or technical reports, for stronger understandability and usability. To evaluate generalizability comparatively, we conducted experiments with models from both GPT and DeepSeek. Results demonstrate that the proposed system can design feasible robots with control strategies when appropriate task inputs are provided, exhibiting substantial potential for enhancing the efficiency and accessibility of robotic system development in research and industrial applications.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 17 of its 51 references that are in the map. First posted on arXiv 9 May 2025.

Builds on

17 of 51 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024TwoStep: Multi-agent Task Planning using Classical Planners and Large Language Models25 Mar
  2. 2023SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models18 Sep

Other references · 15

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  2. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 3,050
  3. 2023Guiding Pretraining in Reinforcement Learning with Large Language Modelscited by 187
  4. 2023AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkerscited by 113
  5. 2023Scalable Multi-Robot Collaboration with Large Language Models: Centralized or Decentralized Systems?cited by 106
  6. 2023ISR-LLM: Iterative Self-Refined Large Language Model for Long-Horizon Sequential Task Planningcited by 77
  7. 2023GPT-4V(ision) for Robotics: Multimodal Task Planning from Human Demonstrationcited by 77
  8. 2024Understanding Large-Language Model (LLM)-powered Human-Robot Interactioncited by 43
  9. 2025Large Language Models for Multi-Robot Systems: A Surveycited by 27
  10. 2024YOLO-MARL: You Only LLM Once for Multi-Agent Reinforcement Learningcited by 9
  11. 2024Deep Reinforcement Learning with Enhanced PPO for Safe Mobile Robot Navigationcited by 7
  12. 2025Natural Multimodal Fusion-Based Human-Robot Interaction: Application With Voice and Deictic Posture via Large Language Modelcited by 4
3 more
  1. 2025Automatic MILP Model Construction for Multi-Robot Task Allocation and Scheduling Based on Large Language Modelscited by 3
  2. 2020Imitation Learning for Autonomous Trajectory Learning of Robot Arms in Spacecited by 2
  3. 2024Deep Reinforcement Learning for Decentralized Multi-Robot Control: A DQN Approach to Robustness and Information Integrationcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 6.

9 papers.

  1. 2025Embracing Trustworthy Brain-Agent Collaboration as Paradigm Extension for Intelligent Assistive Technologiescited by 5
  2. 2025AgentBalance: Backbone-then-Topology Design for Cost-Effective Multi-Agent Systems under Budget Constraintscited by 4
  3. 2025A Hierarchical Agentic Framework for Autonomous Drone-Based Visual Inspectioncited by 3
  4. 2026Diffusing to Coordinate: Efficient Online Multi-Agent Diffusion Policiescited by 3
  5. 2026ROSClaw: A Hierarchical Semantic-Physical Framework for Heterogeneous Multi-Agent Collaborationcited by 1
  6. 2026Episodic Memory Temporal Consistency for Cooperative Multi-Agent Reinforcement Learningcited by 0
  7. 2026HEART: Coordination of Heterogeneous Expert Agents for Physically Grounded Robotic Task Planningcited by 0
  8. 2026DART: A DAG-Based Reputation and Incentive Framework via Blockchain-Enabled Governance for Trustworthy LLM Multi-Agent Collaborationcited by 0
  9. 2026Flowing Faster to Coordinate: One-Step Online Multi-Agent Flow Policiescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Towards Embodied Agentic AI: Review and Classification of LLM- and VLM-Driven Robot Autonomy and Interaction
  2. 2026Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning
  3. 2026RoboSolver: A Multi-Agent Large Language Model Framework for Solving Robotic Arm Problems
  4. 2024BMW Agents -- A Framework For Task Automation Through Multi-Agent Collaboration
  5. 2025AuDeRe: Automated Strategy Decision and Realization in Robot Planning and Control via LLMs
  6. 2025Fundamentals of Building Autonomous LLM Agents
  7. 2024Two Heads Are Better Than One: Collaborative LLM Embodied Agents for Human-Robot Interaction
  8. 2025Large language model-based task planning for service robots: A review