Speaking to Robots: Natural Language Control for Quadrupedal Machines

Author: Denis Avetisyan

Researchers are demonstrating how large language models can translate everyday commands into complex movement plans for quadruped robots, opening the door to more intuitive robot control.

A system architecture facilitates the generation of movement plans, establishing a foundational framework for robotic action.

A distributed control architecture leveraging large language models enables high-success-rate navigation of quadruped robots using natural language instructions.

Despite advances in robotics, intuitive control of quadruped robots remains a significant challenge, often requiring specialized expertise. This paper, ‘Quadrupped-Legged Robot Movement Plan Generation using Large Language Model’, introduces a novel distributed control architecture that leverages Large Language Models to enable natural language-based navigation for these complex machines. Experimental results demonstrate over 90% success in varied indoor scenarios, validating the feasibility of offloading LLM-based planning to achieve robust autonomous operation. Could this approach unlock widespread accessibility and deployment of quadruped robots in real-world environments?

From Programming to Understanding: The Evolution of Robotic Control

Historically, robotics has been constrained by the necessity of painstakingly programming each desired behavior. These hand-engineered control systems, while precise in controlled settings, struggle with the inherent unpredictability of real-world environments. A robot designed to perform a specific task along a fixed path falters when confronted with unexpected obstacles, altered lighting, or even slight variations in object placement. This inflexibility arises because traditional methods lack the capacity to generalize beyond pre-defined scenarios; every contingency requires explicit, pre-programmed instructions. Consequently, robots operating in dynamic spaces often require constant human intervention or are limited to highly structured tasks, hindering their potential for broader application and autonomous operation.

LLM Control represents a significant departure from conventional robotics, where pre-programmed behaviors often struggle with real-world variability. This innovative approach utilizes the power of Large Language Models (LLMs) to directly translate natural language instructions into actionable robotic movements. Rather than relying on painstakingly crafted code for each task, the system empowers robots to understand commands expressed in everyday language – such as “bring me the red block” or “carefully place the object on the shelf”. The LLM effectively serves as a cognitive bridge, interpreting the intent behind the language and generating the necessary control signals for the robot to execute the desired action. This capability promises a future where robots can respond dynamically to unstructured environments and adapt to unforeseen circumstances, all guided by the clarity and flexibility of human language.

The ambition driving LLM Control extends beyond simple command execution; it seeks to imbue robots with genuine comprehension of human intent, even when expressed through ambiguous or complex instructions. Traditional robotics struggles with unstructured commands – requests lacking precise coordinates or predefined sequences – but this approach grounds language directly in physical action. By learning the relationship between linguistic descriptions and resultant movements, the system translates nuanced phrasing into appropriate robotic behavior. This enables a robot to not just respond to a command like “clear the table,” but to understand it encompasses identifying objects, navigating obstacles, and manipulating items with appropriate dexterity-essentially bridging the semantic gap between human communication and robotic execution, paving the way for more intuitive and versatile interactions.

The efficacy of LLM Control hinges on a carefully designed Distributed Control Architecture, which addresses the computational demands of large language models without sacrificing real-time robotic responsiveness. This system intelligently partitions processing tasks; complex reasoning and long-horizon planning are offloaded to powerful external servers, while critical, time-sensitive actions – such as motor control and immediate sensor interpretation – are handled onboard the robot itself. This strategic allocation minimizes latency, enabling the robot to react swiftly to changing conditions, and allows for the use of computationally intensive LLMs that would otherwise be impractical for deployment on embedded systems. The architecture doesn’t simply split processing; it optimizes where processing occurs, creating a synergistic relationship between onboard autonomy and cloud-based intelligence, ultimately allowing robots to seamlessly translate complex linguistic commands into fluid, adaptive behaviors.

The web interface allows users to interact with the system using natural language input.

Directing Intelligence: Prompt Engineering for Robotic Action

Large Language Model (LLM) control in robotic systems is achieved not through inherent autonomous planning capabilities, but by utilizing carefully constructed prompts. These prompts function as a means of directing the LLM to emulate a motion planner; the LLM itself does not independently generate plans. The prompt’s structure and content are critical, as they define the task, constraints, and desired output format. By framing the interaction as a planning problem within the prompt, the LLM processes the request and generates a sequence of actions. The effectiveness of this approach is directly proportional to the precision and completeness of the prompt in defining the robotic task and the environment in which it must be executed.

LLM Prompt Engineering is utilized with Vertex AI Gemini to produce structured action plans specifically formatted as JSON. This approach involves crafting precise prompts that instruct the Gemini model to output data adhering to a predefined JSON schema. The resulting JSON output includes key-value pairs representing robotic actions, parameters, and sequential execution steps. This structured format is critical for automated parsing and integration with robotic control systems, enabling the translation of natural language instructions into machine-readable commands without requiring manual intervention or complex post-processing.

JSON output from the LLM is critical for robotic control due to its structured data format, enabling direct parsing and interpretation by the robot’s control system. This eliminates the need for complex natural language processing on the robot side, instead allowing the control system to directly map JSON keys to specific robot actions or parameters. Specifically, each key-value pair within the JSON represents a command or a parameter setting – for example, “move_joint_1”: “45_degrees” – which can be directly translated into actuator commands. This structured approach ensures reliable and predictable robot behavior, minimizing the risk of misinterpretation inherent in processing unstructured language and facilitating real-time execution of complex action plans.

The SayCan framework addresses the challenge of translating high-level, natural language instructions into a series of executable robotic actions by establishing a connection between language and action primitives. It operates by predicting the feasibility of different action sequences, assessing whether a robot can physically perform each step given its kinematic and dynamic limitations, and then selecting the most viable sequence that satisfies the given instruction. This is achieved through a probabilistic model that scores potential action plans based on their likelihood of successful execution, effectively grounding abstract commands into concrete, physically realizable robotic behaviors. The framework’s core function is to bridge the semantic gap between human intent and robotic capability, enabling robots to interpret and act upon complex instructions in unstructured environments.

Mapping Reality: Perception, Localization, and System Integration

The Perception Host utilizes the Robot Operating System (ROS) and the HDLLocalization module to create a three-dimensional representation of the surrounding environment. This process involves sensor data fusion – integrating information from sources like LiDAR and cameras – to build a detailed map. Simultaneously, HDLLocalization algorithms accurately determine the robot’s position within that map, achieving centimeter-level precision. This localization is not merely positional; it also incorporates orientation data, providing a complete six degrees-of-freedom pose estimate crucial for robust navigation and task execution. The resulting 3D map and pose information are continuously updated, enabling the robot to adapt to dynamic environments and maintain awareness of its surroundings.

Semantic Waypoints are pre-defined locations within the operational environment, each associated with a specific set of global coordinates – typically expressed as latitude, longitude, and altitude. These waypoints are not simply positional markers; they incorporate semantic meaning, representing recognizable locations or tasks, such as “Loading Dock,” “Entrance,” or “Inspection Area.” The robot’s navigation system utilizes these geolocated semantic labels as crucial reference points, enabling it to interpret high-level navigation commands and accurately plan paths to designated areas without requiring constant human intervention or detailed map interpretation. The precision of the global coordinates ensures consistent and repeatable navigation performance across multiple runs and facilitates integration with other geospatial data sources.

The Motion Host functions as the low-level control interface for the robotic system. It receives high-level motion plans from the Large Language Model (LLM) and translates these into specific commands for the robot’s actuators – including motors, steering mechanisms, and other effectors. Simultaneously, the Motion Host gathers data from the robot’s sensors – such as wheel encoders, IMUs, and force sensors – providing feedback for closed-loop control and ensuring accurate plan execution. This host manages the real-time interaction with the robot’s hardware, bridging the gap between the LLM’s abstract plans and the physical actions of the robot.

The Flask Web Server functions as the primary interface for user interaction with the robotic system. It receives natural language commands from a user, which are then processed and relayed to the LLM for plan generation. This server utilizes the Flask framework, a lightweight Python web application framework, to create a responsive and accessible user interface. The server handles the communication between the user and the backend systems, including receiving the initial command, transmitting it for processing, and potentially returning status updates or results to the user. This bidirectional communication loop enables users to control the robot’s actions through intuitive language input, completing the system’s overall control flow.

Demonstrating Capability: Navigational Performance and Validation

Rigorous testing of the LLM-based control system involved a tiered navigational challenge, progressing from simple Single-Room Navigation to the complexities of Multi-Room scenarios and ultimately, the demanding task of Cross-Zone Navigation. This experimental design allowed for a systematic evaluation of the system’s capabilities as environmental demands increased. Each scenario presented unique obstacles – from spatial awareness within a confined space to path planning across multiple interconnected rooms and, finally, navigating between distinct zones with varying layouts – ensuring a comprehensive assessment of the LLM’s adaptability and robustness in increasingly complex environments. The progression facilitated a granular understanding of the system’s strengths and limitations at each stage of navigational difficulty.

Rigorous evaluation of the navigation system centered on two key performance indicators: Task Completion Time and Success Rate. Success Rate quantified the proportion of trials where the agent successfully reached its designated goal, providing a clear measure of navigational reliability. Simultaneously, Task Completion Time, measured in seconds, detailed the efficiency with which the agent executed the task, offering insights into the system’s speed and responsiveness. These metrics, gathered across varied navigational challenges – from single-room scenarios to complex multi-room and cross-zone environments – allowed for a comprehensive assessment of the LLM-based control system’s performance, highlighting both its ability to consistently reach destinations and the temporal efficiency of its path planning.

Experimental validation reveals a robust capacity for navigation using the developed LLM-based control system. Across fifteen trials within a single-room environment, the system consistently achieved task completion, demonstrating a perfect 100% success rate. This indicates a foundational ability to interpret navigational commands and execute them accurately in a simplified setting, establishing a crucial baseline for performance in more intricate scenarios. The consistent success within this controlled environment suggests the LLM effectively processes spatial information and translates it into appropriate motor actions, paving the way for reliable operation in complex, multi-room layouts and cross-zone navigation challenges.

Evaluations within more complex navigational scenarios reveal a robust performance capability. Across twenty-five trials involving short navigation between multiple rooms, the system achieved a 96% success rate, demonstrating effective path planning and obstacle avoidance in interconnected spaces. Furthermore, the control system consistently executed cross-zone navigation flawlessly, attaining a perfect 100% success rate over twenty trials – indicating a reliable capacity to traverse distinct environmental zones and maintain navigational accuracy even with increased spatial complexity. These results collectively suggest the system’s adaptability and effectiveness in handling increasingly challenging real-world navigational demands.

Navigation performance metrics reveal a clear correlation between environmental complexity and task completion time. Initial trials in a single-room setting demonstrated an average task completion time of 45.26 seconds, establishing a baseline for efficient movement. As the navigational challenge increased with the introduction of multiple rooms, the average time extended to 68.27 seconds, indicating a moderate increase in cognitive load and path planning demands. The most complex scenario, Cross-Zone Navigation, required an average of 130.98 seconds to complete, reflecting the system’s ability to adapt to significantly more intricate spatial reasoning and long-distance traversal-a testament to the LLM-based control system’s robustness in dynamic environments.

Toward Intelligent Autonomy: Future Directions and Expanding Capabilities

Future advancements in robotic control are poised to leverage the power of Visual Language Models, enabling robots to interpret their surroundings through visual data much like humans do. These models bridge the gap between image processing and natural language understanding, allowing a robot to not simply see an object, but to comprehend its properties and relationships within a scene. By combining visual input with the reasoning capabilities of Large Language Models, robots can move beyond pre-programmed responses and begin to dynamically adapt to novel situations, identify relevant objects, and ultimately, execute complex tasks based on what they “see”. This integration promises a significant leap towards creating robots capable of genuine environmental awareness and truly autonomous operation in unstructured, real-world settings.

Retrieval Augmented Generation represents a significant advancement in enhancing Large Language Model (LLM) performance within robotic systems. Rather than relying solely on the LLM’s pre-existing knowledge, RAG equips the robot with the ability to access and incorporate information from a history of prior prompts and interactions. This contextual awareness allows the LLM to generate more accurate, relevant, and nuanced responses, particularly when faced with ambiguous or complex commands. By effectively ‘remembering’ past instructions and environmental feedback, the system avoids repetitive errors and builds upon previous learning, ultimately leading to more robust and adaptable robotic behavior. The implementation of RAG moves beyond static knowledge, enabling a dynamic and evolving understanding of tasks and surroundings, and paving the way for robots capable of sustained, intelligent operation.

Ongoing development centers on iterative improvements to the foundational system architecture and control algorithms, with the ultimate goal of realizing the complete capabilities of Large Language Model (LLM)-driven robotic control. This involves not simply increasing computational power, but also optimizing the interplay between the LLM and the robot’s physical actions, focusing on areas like efficient task decomposition, robust error recovery, and enhanced generalization to novel situations. Researchers are exploring methods for continuous learning, allowing the robotic system to refine its performance over time through interaction with the environment and feedback from successful or unsuccessful attempts, ultimately striving for a level of adaptability and intelligence previously unattainable in robotic systems.

The culmination of this work suggests a future where robots move beyond pre-programmed tasks and demonstrate genuine adaptability in unpredictable settings. By leveraging large language models, these robots are poised to interpret complex environments, understand nuanced instructions, and formulate plans without constant human intervention. This shift promises a new generation of robotic systems capable of operating effectively in dynamic, real-world scenarios – from assisting in disaster relief and conducting remote exploration to providing personalized support in homes and workplaces. Ultimately, this research aims to unlock a level of robotic intelligence that fosters seamless collaboration between humans and machines, expanding the possibilities for automation and problem-solving across diverse fields.

The presented work emphasizes a holistic approach to quadruped robot control, mirroring the interconnectedness of complex systems. This aligns perfectly with Carl Friedrich Gauss’s observation: “I do not know what I appear to the world, but to myself I seem to be a man who has lived a long life without knowing it.” The research demonstrates that a seemingly simple interface – natural language commands – can unlock sophisticated robotic behaviors only when integrated within a carefully designed distributed control architecture. Just as Gauss’s quote suggests a deeper, underlying reality beneath surface appearances, this paper reveals that robust robot navigation stems not from isolated algorithms, but from the harmonious interplay of multiple components – LLM prompt engineering, ROS integration, and a thoughtfully constructed system-a structure dictating behavior, as the article clearly illustrates.

Beyond the Prompt

The successful marriage of Large Language Models and quadrupedal locomotion demonstrated here is less a solution, and more a revealing of deeper structural challenges. The system functions, undeniably, but its reliance on meticulously engineered prompts hints at a fundamental disconnect. The robot doesn’t understand instruction; it correlates tokens. A truly robust system will require a move away from brittle prompt dependence, towards internal representations that map natural language to actionable, embodied plans. The current architecture, while achieving notable success in controlled indoor environments, exposes the fragility inherent in transferring learned behaviors to unforeseen circumstances.

Future work must address the limitations of purely reactive control. Integrating predictive modeling, allowing the robot to anticipate environmental changes and proactively adjust its gait, will be crucial. Furthermore, the elegance of a distributed control architecture demands careful consideration of inter-module communication. Current methods, however effective, remain susceptible to cascading errors. The focus should shift from simply achieving a task, to building a system capable of graceful degradation and self-diagnosis.

Ultimately, the true measure of this work lies not in its immediate performance, but in the questions it provokes. The path forward necessitates a more holistic approach, recognizing that intelligence, even in a robotic form, is not merely a matter of scaling parameters, but of designing systems that reflect the inherent simplicity and resilience of living organisms.

Original article: https://arxiv.org/pdf/2512.21293.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/