Author: Denis Avetisyan
Researchers are exploring how large language models can unlock more intuitive and flexible control of unmanned aerial vehicles, moving beyond traditional remote operation.
This paper introduces UAV-GPT, a dual-agent framework utilizing large language models for advanced task planning, tool invocation, and ROS integration in human-UAV interaction.
While current Human-UAV Interaction (HUI) systems struggle with adaptability and personalized task execution, this paper, ‘Chat with UAV — Human-UAV Interaction Based on Large Language Models’, introduces a novel framework-UAV-GPT-that leverages Large Language Models to enable more natural and flexible communication with drones. By decoupling task planning from execution via a dual-agent architecture, UAV-GPT intelligently invokes tools and navigates complex scenarios, significantly improving HUI smoothness and task flexibility. This approach demonstrates enhanced performance across diverse applications, but how can such LLM-driven frameworks be further scaled and integrated into real-world operational environments?
The Limitations of Conventional Robotic Architectures
Conventional robotics frameworks, such as the widely adopted Three-Layer Architecture, often falter when confronted with the nuances of real-world interaction. These systems typically separate deliberation, sequencing, and execution, creating a rigid structure ill-equipped to handle the ambiguity of natural language or the unpredictability of dynamic environments. The layered approach struggles to seamlessly integrate high-level cognitive tasks – like interpreting complex user requests – with low-level motor control, resulting in delayed responses and brittle performance. Consequently, a simple, unanticipated obstacle or a slightly rephrased command can disrupt the entire system, highlighting the limitations of architectures not designed for fluid, adaptive interaction with humans and ever-changing surroundings. The inherent sequential nature of these systems also creates bottlenecks, preventing rapid responses crucial for effective collaboration in complex scenarios.
Truly effective Human-UAV Interaction (HUI) transcends the limitations of scripted responses, necessitating systems capable of discerning user intent and dynamically adjusting to unpredictable real-world scenarios. Current approaches often rely on pre-defined commands, proving brittle when confronted with ambiguity or novel situations; a user stating “investigate that area” requires the UAV to interpret “that area,” assess potential obstacles, and formulate an appropriate search pattern – tasks exceeding simple command execution. Research indicates that successful HUI hinges on integrating advanced cognitive architectures, including natural language processing and machine learning, allowing UAVs to not merely react to instructions, but to understand the underlying goals and proactively adapt their behavior in response to changing environmental conditions and unforeseen events. This shift toward intent-based control promises more intuitive, robust, and ultimately, more useful interactions between humans and unmanned aerial vehicles.
UAV-GPT: A Language-Driven Framework for Intelligent Control
UAV-GPT represents a new approach to Human-UAV Interaction, leveraging the capabilities of Large Language Models (LLMs) to enable more intuitive control and task assignment. This framework departs from traditional methods by utilizing LLMs not merely for speech recognition or simple command execution, but as the core of the entire interaction process. The system is structured around two distinct LLM-based agents working in concert to translate human instructions into actionable UAV behaviors. This design prioritizes a natural language interface, allowing users to communicate task objectives in a manner more akin to human-to-human communication than traditional programmatic control schemes, and seeks to provide greater flexibility in handling varied and complex scenarios.
UAV-GPT utilizes a dual-agent system centered around Large Language Models (LLMs) to process and execute user requests. The initial Task Planning Agent receives natural language commands and is responsible for their interpretation and classification into actionable tasks. This agent then relays these tasks to the Execution Agent, a separate LLM-based component. The Execution Agent translates the planned tasks into specific UAV control instructions, managing the drone’s actions to fulfill the original user command. This sequential processing, decoupling planning from execution, allows for a modular approach to complex operations and facilitates adaptability to varying task requirements.
UAV-GPT’s modular design is achieved through the separation of task planning and execution into distinct LLM-based agents. This decoupling allows for independent optimization of each component; the planning agent focuses solely on interpreting user intent and formulating a task sequence, while the execution agent concentrates on translating those plans into actionable UAV commands. Consequently, the framework exhibits enhanced adaptability as either agent can be updated or replaced without affecting the functionality of the other. Furthermore, complex tasks are simplified by breaking them down into manageable planning and execution stages, reducing the computational burden on individual components and improving overall system robustness.
Quantitative Validation of Real-Time Control Performance
The LLM-Based Execution Agent utilizes the Robot Operating System (ROS) as the foundation for its control algorithms, enabling robust and reliable UAV operation. Specifically, the EgoPlanner algorithm is integrated to facilitate safe and accurate flight maneuvers, including path planning and trajectory optimization. EgoPlanner provides functionalities for both global path planning, considering the overall mission objectives, and local reactive planning, dynamically adjusting to unforeseen obstacles and environmental changes. This ROS-based architecture allows for modularity, scalability, and integration with a wide range of sensors and actuators commonly used in unmanned aerial vehicle systems, contributing to the agent’s ability to perform complex tasks while maintaining situational awareness and avoiding collisions.
System performance is quantitatively assessed using two primary metrics: Task Execution Success Rate (ESR) and Intent Recognition Accuracy (IRA). ESR represents the percentage of assigned tasks completed successfully by the UAV, providing a direct measure of operational effectiveness. Intent Recognition Accuracy (IRA) quantifies the system’s ability to correctly interpret high-level commands or requests, indicating the reliability of the natural language interface. These metrics are tracked to provide verifiable, data-driven evidence of the system’s capabilities and to facilitate performance comparisons against baseline models and alternative architectures.
Quantitative evaluation demonstrates that UAV-GPT significantly outperforms single-agent LLM frameworks in key performance indicators. Specifically, UAV-GPT achieved a 45.5% improvement in Task Execution Success Rate (ESR), indicating a higher proportion of successfully completed tasks. Furthermore, Intent Recognition Accuracy (IRA) increased by 24% compared to baseline single-agent systems, demonstrating improved ability to correctly interpret and act upon user or environmental intentions. These gains were measured through standardized testing procedures and provide empirical evidence of UAV-GPT’s enhanced operational capabilities.
UAV-GPT is designed with a modular architecture to facilitate integration with established skill-based frameworks. Specifically, the system is compatible with the CAP (Cognitive Architecture for Planning) Framework and PromptCraft, allowing developers to leverage pre-built skills and prompting strategies. This integration expands UAV-GPT’s capabilities beyond its core LLM functionality, enabling more complex task decomposition, improved planning, and enhanced robustness in dynamic environments. The modular design also simplifies the process of incorporating new skills and adapting the system to different UAV platforms and applications.
Towards Autonomous Adaptation and Sustainable Efficiency
The UAV-GPT system incorporates a federated learning architecture, allowing for continuous performance enhancements without compromising data privacy. This decentralized approach enables individual unmanned aerial vehicles (UAVs) to refine the system’s parameters based on their unique operational experiences, and then share only these refined parameters – not the raw data itself – with a central server. This collaborative learning process avoids the need for a centralized dataset, mitigating security risks and reducing bandwidth requirements. Over time, this iterative process of local learning and parameter aggregation results in a continually improving model capable of optimizing performance across diverse and evolving conditions, ultimately fostering greater autonomy and adaptability in UAV operations.
UAV-GPT distinguishes itself through a core design principle: optimizing not simply for successful task completion, but also for minimizing the energy footprint of unmanned aerial vehicles. This focus on UAV Energy Consumption (UEC) moves beyond traditional performance metrics, acknowledging the growing need for sustainable and efficient drone operations. By actively considering power usage throughout the flight process – from path planning to dynamic adjustments based on environmental factors – the system strives to extend flight times and reduce overall operational costs. This commitment to energy efficiency isn’t merely an added benefit; it’s integral to UAV-GPT’s architecture, paving the way for longer-duration deployments and broader applicability in diverse fields such as environmental monitoring, infrastructure inspection, and delivery services.
Significant gains in operational efficiency are demonstrated by UAV-GPT, which achieves a remarkable 62.5-watt reduction in energy consumption per task when contrasted with conventional, single-agent Large Language Model (LLM) frameworks. This reduction isn’t merely a marginal improvement; it represents a substantial leap toward sustainable drone operations, allowing for extended flight times and broader deployment possibilities. The system’s ability to optimize energy usage while maintaining task performance highlights the effectiveness of its federated learning architecture and intelligent control algorithms, suggesting a pathway for minimizing the environmental impact of unmanned aerial vehicles and maximizing their utility across various applications.
UAV-GPT represents a significant step forward in how humans and unmanned aerial vehicles collaborate, moving beyond pre-programmed responses to enable genuinely interactive operation. The system integrates the reasoning capabilities of large language models with established, reliable control algorithms, allowing UAVs to not merely execute commands, but to understand intent and adapt to complex, real-world scenarios. This fusion unlocks potential across diverse applications, from more intuitive search and rescue operations where UAVs can interpret nuanced requests and navigate challenging environments, to streamlined infrastructure inspection where they can autonomously adjust scanning parameters based on visual feedback and operator guidance. Ultimately, UAV-GPT facilitates a more natural and effective partnership between humans and UAVs, paving the way for greater autonomy and broader integration into daily life.
The pursuit of seamless Human-UAV Interaction, as demonstrated by UAV-GPT, echoes a fundamental tenet of mathematical rigor. The framework’s dual-agent architecture, separating task planning from execution, embodies a desire for provable correctness, not merely functional performance. This aligns with the principle that true elegance arises from minimizing complexity while maximizing reliability. As Carl Friedrich Gauss once stated, “If other mathematicians had not already invented it, I would have.” The sentiment underscores the importance of building upon established principles – in this case, robust task decomposition and tool invocation – to achieve scalable and dependable systems. The system’s ability to intelligently manage obstacles and execute complex tasks through natural language highlights a solution striving for mathematical purity in its design.
What’s Next?
The presented work, while demonstrating a functional interface, merely skirts the fundamental challenges of truly intelligent autonomy. The separation of planning and execution, mediated by a Large Language Model, is a pragmatic compromise, not an architectural virtue. It invites scrutiny: where does the responsibility for error lie when the ‘understanding’ of a command deviates from its intended consequence? The current reliance on tool invocation, while enabling task completion, feels suspiciously like a sophisticated form of scripted behavior, elegantly masking a lack of genuine reasoning.
Future efforts must move beyond demonstrable functionality and embrace formal verification. The inherent ambiguity of natural language demands a rigorous mapping to provably correct action sequences. The current framework’s dependence on empirical testing-‘does it work in this scenario?’-is insufficient. A truly robust system necessitates a mathematical guarantee of safety and correctness, even-or especially-in unforeseen circumstances. The illusion of conversational interaction should not overshadow the necessity of algorithmic precision.
Furthermore, the integration with Robotic Operating System (ROS) represents a practical convenience, but it also introduces a layer of complexity that obscures the core computational problems. The field should not mistake successful integration for fundamental progress. The pursuit of ‘natural’ interaction is commendable, yet the ultimate measure of success will be a system that doesn’t merely respond to commands, but understands their implications, and acts accordingly-with verifiable certainty.
Original article: https://arxiv.org/pdf/2512.08145.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
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2025-12-10 15:12