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

Lihuang Chen, Xiangyu Luo, Jun Meng

arXiv:2512.10605v2cs.RO

TL;DR

Existing robot task-planning programs can be inflexible and difficult to generalize across tasks. LEO-RobotAgent gives LLMs a streamlined plan-and-act framework with configurable tools and human interaction; experiments report applicability across robot types and task complexities, while identifying weak spatial common sense as a limitation.

  • Problem

    Traditional task planning relies on manually designed programs that can become complex and do not generalize or transfer well across tasks.

    Source: §1 · p. 1

  • Method

    LEO-RobotAgent uses an LLM-driven iterative planning and action framework with configurable tools and optional human intervention.

    Source: §1 · p. 1, §3 · p. 2, §3.1 · p. 3

  • Results

    Experiments verify applicability across robot types and task complexities, while the authors report that reasoning guidance and task-related examples improve planning and that streamlined frameworks better unleash LLM capabilities.

    Source: §1 · p. 2, §5 · pp. 7–8

  • Takeaways & Limitations

    The framework’s configurable tools and application system are presented as supporting deployment across diverse robotic tasks and lowering the threshold for human-robot interaction.

    Source: §1 · p. 2

    Weak spatial common-sense understanding in current LLMs means agents need 3D-space processing strategies for correct control operations.

    Source: §5 · p. 8

Abstract

from arXiv · show

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.

1 Introduction

Task-specific robot programs can be inflexible, difficult to scale, and hard to transfer across tasks. LEO-RobotAgent proposes a general-purpose agent framework that lets LLMs plan and act while supporting human interaction and customizable tools.

1 Introduction

  • Task-specific programs require manually designed logic, become bloated as tasks grow more complex, and do not generalize or transfer well across tasks.Prior work has applied LLMs to planning on platforms including UAVs and robotic arms, [2].Source: §1 · p. 1
  • LLMs are proposed as a foundation for high-level robot planning that can reason beyond manually predefined rules and plan behavior for diverse objectives.The authors argue that world knowledge and cognitive capabilities can support self-driven behavioral planning.Source: §1 · p. 1
  • LEO-RobotAgent lets LLMs plan, execute actions, and reflect iteratively, with optional user intervention during task execution.The framework also compares agent designs and explores deployment and communication guidance for ordinary users.Source: §1 · p. 1
  • The contributions include a configurable tool-based application system and tests across robot types, task complexities, and scenarios.The framework also uses prompt-engineering techniques to probe planning capabilities and identify challenges.Source: §1 · p. 2

2 Related Work

Related work spans LLMs used for auxiliary robotic tasks, task planning and execution, and embodied reasoning or benchmarking. The authors identify a gap in broad robot and task coverage, autonomy, debugging stability, and continued human interaction.

2 Related Work

  • LLMs and VLMs have supported auxiliary robotic tasks including visual understanding, UAV flight recommendations, disaster-relief planning, and urban patrol analysis.The cited examples span information integration, perception, and planning roles –.Source: §2 · p. 2
  • Task-planning work ranges from executable program generation and direct robotic-arm actions to agent frameworks with stepwise planning and execution.Some agent approaches divide responsibilities among LLMs, while another uses multilevel planning and feedback for error correction –.Source: §2 · p. 2
  • Embodied-reasoning models and VLA benchmarks represent alternatives to the paper’s tool-based use of off-the-shelf LLMs.The paper characterizes its tool-based approach as lowering deployment barriers and improving interpretability, while noting dependence on toolset capabilities,.Source: §2 · p. 2
  • The authors identify limited robot-type and task coverage, low autonomy, debugging difficulty, instability, and little support for post-deployment human interaction as research challenges.They also note that generated programs may require rigorous validation and cannot handle tasks requiring natural-language understanding.Source: §2 · p. 2

3 Methodology

LEO-RobotAgent uses an LLM-driven feedback loop in which users provide task context, the model reasons and invokes tools, and observations inform subsequent actions. Its robot-specific tools and interactive ROS-based system support configurable capabilities and user intervention.

3 Methodology

  • The framework’s core loop connects user task descriptions, LLM reasoning and tool actions, returned observations, and optional user intervention.The paper presents this as a streamlined, flexible cycle and distinguishes its basic schematic from implementation and system details.Source: §3 · p. 2

3.1 Agent for Robotics

  • LLM responses are constrained to JSON with a reasoning message followed by an action and its input parameters.The message communicates planning or assessment, while the action fields specify what to execute and with which inputs.Source: §3.1 · pp. 2–3
  • Users can register task-specific tools for robot control, perception, and environmental interaction, with each tool specifying its name, function, description, formats, and availability.Tool observations can report detections, waypoint arrival, motion-planning success, or task analysis.Source: §3.1 · p. 3
  • The agent accumulates task, model-output, tool-observation, and user-input history, then ends the loop with a completion report or interruption cause.Termination occurs when the LLM determines that the task is complete or cannot proceed further.Source: §3.1 · p. 3
  • During execution, users can correct errors, guide the agent, modify task content, or direct the next phase when collaboration is permitted.The authors describe this as two-way human-robot interaction.Source: §3.1 · p. 3

3.2 Complete Agent System

  • The complete system integrates the agent and toolset as ROS nodes, with a web interface for user communication and system operations.Registered tools can include robot-control and perception nodes, RAG, and simulation environments; ROS topics support dialogue, control, perception, and tool feedback, while WebSocket-based operations support configuration and debugging.Source: §3.2 · pp. 3–4

4 Experiments

Experiments evaluated LEO-RobotAgent through UAV simulation and real-world tasks, prompt comparisons, and architecture comparisons across tasks of varying complexity. Results report feasibility and sim-to-real transfer, prompt-dependent search outcomes, and strong performance from the streamlined agent architecture.

4.1 Feasibility Verification and Real UAV Experiment

  • Across ten trials, the UAV succeeded in 9 simulation attempts and 7 real-world attempts; real failures involved flight-control accuracy and object-localization errors.The authors report straightforward sim-to-real transfer, with the gap largely depending on the robot and implemented tools.Source: §4.1 · p. 4, Figure 4 · p. 5

4.2 Prompt Experiment

  • Combining CoT and one-shot produced the best prompt results; one-shot was fastest on successful runs, while CoT added token and time overhead.Each method was tested ten times per scenario, and the combined approach produced thorough, efficient search paths with similar trends in urban search.Source: §4.2 · pp. 4–5, Table 1 · p. 5
  • Search maps show ineffective boundary-facing searches without prompts, example-mimicking one-shot paths that miss corners, and more divergent CoT searches.The combined approach produced thorough, efficient paths; the authors recommend providing task information, error avoidance, examples, and reasoning guidance when possible.Source: §4.2 · pp. 4–5, Figure 6 · p. 6

4.3 Agent Framework Architecture Comparison Experiment

  • LEO-RobotAgent performed promisingly on Task 1 and scored consistently highly with low variance across three tasks, using less time and tokens on Tasks 2–3.The LEO (human) setting significantly improved performance, while increasing token usage and execution time; the authors also report fewer hallucinations and stable long-term memory for their framework.Source: Table 3 · p. 7, §4.3 · p. 7
  • For simple Task 1, DAS and CGE were stable, low-overhead options, while DLLMs and TLLMs scored poorly amid coordination costs, hallucinations, and omitted task details.The comparison used the same model and task descriptions, with 16 experiments per scheme in each scenario.Source: §4.3 · p. 7

5 Conclusion

LEO-RobotAgent is a streamlined, general-purpose framework for robotic embodied operators, with versatility across robot types, task scenarios, and task contents. Its contribution centers on system integration and practical deployment, while experiments identify weak spatial common-sense understanding as a limitation.

5 Conclusion

  • The framework exhibits versatility across robot types, task scenarios, and task contents, with a high degree of autonomy.The paper places its core contribution in system-level integration, robust architecture, and practical deployment rather than fundamentally novel foundational learning algorithms.Source: §5 · p. 7
  • Sufficient reasoning guidance and task-related information, such as a reference example, significantly improve task-planning performance.Source: §5 · p. 7
  • Agent comparisons suggest a streamlined framework can better unleash LLM capabilities and reduce users’ debugging burden.A single LLM handles multiple responsibilities across reasoning and action, while auxiliary LLMs or VLMs can serve as tools for summarization and perception.Source: §5 · p. 8
  • Weak spatial common-sense understanding means LLMs need 3D-space processing strategies to produce correct control operations.The paper identifies real-world spatial cognition as a key direction for improving robotic agent frameworks.Source: §5 · p. 8
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