mlsanityData to 7 Oct 2026

LEO-RobotAgent: A General-purpose Robotic Agent for Language-driven Embodied Operator

arXiv 2512.10605 ↗First posted 11 Dec 2025cs.ROcited by 0

On the map: LLM agents and tool use → Multimodal and embodied agents

Abstract (arXiv)

We propose LEO-RobotAgent, a general-purpose language-driven intelligent agent framework for robots. Under this framework, LLMs can operate different types of robots to complete unpredictable complex tasks across various scenarios. This framework features strong generalization, robustness, and efficiency. The application-level system built around it can fully enhance bidirectional human-robot intent understanding and lower the threshold for human-robot interaction. Regarding robot task planning, the vast majority of existing studies focus on the application of large models in single-task scenarios and for single robot types. These algorithms often have complex structures and lack generalizability. Thus, the proposed LEO-RobotAgent framework is designed with a streamlined structure as much as possible, enabling large models to independently think, plan, and act within this clear framework. We provide a modular and easily registrable toolset, allowing large models to flexibly call various tools to meet different requirements. Meanwhile, the framework incorporates a human-robot interaction mechanism, enabling the algorithm to collaborate with humans like a partner. Experiments have verified that this framework can be easily adapted to mainstream robot platforms including unmanned aerial vehicles (UAVs), robotic arms, and wheeled robot, and efficiently execute a variety of carefully designed tasks with different complexity levels. Our code is available at https://github.com/LegendLeoChen/LEO-RobotAgent.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. Builds on 23 of its 25 references that are in the map. First posted on arXiv 11 Dec 2025.

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23 of 25 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025General-Purpose Aerial Intelligent Agents Empowered by Large Language Models11 Mar
  2. 2025UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobility4 Jan
  3. 2024MALMM: Multi-Agent Large Language Models for Zero-Shot Robotics Manipulation26 Nov

Other references · 20

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  3. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  4. 2025Qwen3 Technical Reportcited by 8,195
  5. 2023PaLM-E: An Embodied Multimodal Language Modelcited by 2,037
  6. 2024Qwen2.5-Coder Technical Reportcited by 1,151
  7. 2022ProgPrompt: Generating Situated Robot Task Plans using Large Language Modelscited by 598
  8. 2024VLABench: A Large-Scale Benchmark for Language-Conditioned Robotics Manipulation with Long-Horizon Reasoning Taskscited by 171
  9. 2023Task and Motion Planning with Large Language Models for Object Rearrangementcited by 140
  10. 2023Large Language Models for Robotics: A Surveycited by 132
  11. 2023Chat with the Environment: Interactive Multimodal Perception Using Large Language Modelscited by 42
  12. 2023CoPAL: Corrective Planning of Robot Actions with Large Language Modelscited by 39
8 more
  1. 2023Deploying and Evaluating LLMs to Program Service Mobile Robotscited by 36
  2. 2023DisasterResponseGPT: Large Language Models for Accelerated Plan of Action Development in Disaster Response Scenarioscited by 33
  3. 2023Interactive Planning Using Large Language Models for Partially Observable Robotics Taskscited by 27
  4. 2024In-Context Learning Enables Robot Action Prediction in LLMscited by 25
  5. 2023TypeFly: Flying Drones with Large Language Modelcited by 23
  6. 2023REAL: Resilience and Adaptation using Large Language Models on Autonomous Aerial Robotscited by 19
  7. 2023Agent as Cerebrum, Controller as Cerebellum: Implementing an Embodied LMM-based Agent on Dronescited by 9
  8. 2024MageBench: Bridging Large Multimodal Models to Agentscited by 6

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