Grounding Natural Language to MCP Actions: Annotating Voice Commands for Factory Floor Robots

The development of industrial automation and the introduction of Industry 4.0 and Industry 5.0 concepts are significantly changing approaches to human interaction with robotic systems in production. Traditional methods of controlling industrial robots, based on rigidly defined interfaces or programming in specialized languages, often limit the flexibility of production processes and require a high level of technical training of operators.

Recent advances in large language models and protocols for interacting with external systems open new opportunities for developing intelligent robot control mechanisms. In particular, the use of the Model Context Protocol (MCP) allows for the standardization of interactions between language models and sets of available functions or tools.

Analysis of related research

One of the fundamental directions in this area is the problem of language grounding, which involves establishing a correspondence between language constructs and objects, actions, or states of the real environment. In robotics, grounding is used to interpret user commands, taking into account the execution context, spatial constraints, and the system's current state. Early approaches were mainly based on rules, templates, and semantic parsing, which provided controlled behavior but limited the scalability and adaptability of systems.

Further development of machine learning methods and neural networks led to the emergence of models capable of learning to reflect natural language instructions into structured representations of actions. Considerable attention was paid to the use of encoder–decoder architectures, transformers, and instruction-following models, which allow for taking into account both the command text and information about the execution environment.

A separate direction of research is the creation of datasets for human–robot interaction (HRI). Existing corpora typically contain natural language instructions, corresponding robot actions, and environmental context. Most of them focus on navigation tasks, home robotics, or simulation environments, while industrial manufacturing scenarios remain underexplored due to the difficulty of standardizing manufacturing processes and stringent safety requirements.

Problem Statement

In an industrial environment, voice control of robotic systems requires the accurate and unambiguous transformation of operators' natural-language instructions into a set of formalized executive actions.

Within the framework of the study, consider the problem of mapping (grounding) voice commands with actions represented via the Model Context Protocol (MCP). A voice command is understood as a natural language instruction of the operator, obtained after the stage of automatic speech recognition. An MCP-action is defined as a structured description of a robot operation that includes the function name and the parameters required for its execution.

Formally, the problem can be represented as a mapping function:

[Action]:

[

f: (u, c, A) \rightarrow a^*

]

[Action]

where:

• (u) — natural language command of the user;
• (c) — context of the execution environment;
• (A={a_1,a_2,...,a_n}) — set of available MCP-actions;
• (a^*) — most relevant action or sequence of actions.

The context of the environment can include information about the current state of the production line, equipment availability, operation parameters, and the history of previous commands.

To ensure correct training and evaluation of the system, it is necessary to generate an annotated dataset in which each voice command corresponds to a structured representation of the target MCP action. The annotation should reflect not only the main action, but also its arguments, parameters, and possible execution constraints.

Dataset construction

For training and evaluation of the natural language grounding system, an annotated dataset was constructed containing operators’ voice instructions and their structured representations as executable MCP actions. The dataset is designed around common factory-floor interaction scenarios and includes both simple commands and multi-step industrial operations.

An example dataset entry is presented below.

The proposed dataset structure provides a consistent representation of natural language instructions and enables the development and evaluation of models that transform spoken commands into executable MCP actions in industrial environments.

Experimental setup

To evaluate the effectiveness of grounding natural-language voice commands in executable MCP actions, a series of experiments was conducted using the constructed annotated dataset. The evaluation focused on measuring the system's ability to correctly identify the intended action and extract the required parameters from spoken instructions.

The experimental workflow consisted of four sequential stages. First, voice commands were converted into textual representations using an Automatic Speech Recognition (ASR) module. Second, the recognized text was processed by a language understanding component responsible for intent detection and semantic interpretation. Third, the interpreted command was matched against the available set of MCP actions. Finally, the selected action, together with its parameters, was transformed into a structured MCP call.

The evaluation process was conducted on separate subsets of the dataset to ensure an objective assessment of model performance and to avoid overlap between training and testing samples. The experiments considered both the quality of action selection and the correctness of parameter extraction under different command formulations and contextual conditions.

FAQ

What is grounded language data in factory robot interaction?

Grounded language data refers to natural-language commands explicitly linked to executable robotic actions and environmental context. Such data enables robots to interpret operator instructions in a structured and operationally meaningful way.

Why is voice command annotation important for industrial robotics?

Voice command annotation creates structured mappings between spoken instructions and machine-executable actions. This process supports the training and evaluation of language-understanding systems for factory environments.

How does MCP action mapping improve robot control?

MCP action mapping converts natural-language instructions into standardized, executable actions with defined parameters. This approach increases interoperability and consistency between language interfaces and robotic systems.

What information is typically included in a factory robot NLU dataset?

A factory robot NLU dataset usually contains voice commands, detected intents, contextual variables, target actions, and execution parameters. These components allow models to learn semantic relationships between language and robot behavior.

What is the role of spatial grounding labeling in command interpretation?

Spatial grounding labeling associates linguistic expressions with physical positions, objects, and operational zones. It enables robots to execute commands containing location-dependent instructions correctly.

How is command disambiguation data used during annotation?

Command disambiguation data captures cases where multiple actions may correspond to the same spoken instruction. Contextual information is used to determine the intended robotic operation.

What challenges arise when creating grounded language data for factories?

Industrial environments contain noisy audio conditions, operational constraints, and context-sensitive commands. These factors increase annotation complexity and require precise semantic alignment.

How are voice commands transformed into MCP actions?

The transformation process includes speech recognition, language understanding, intent extraction, and action selection. The final output is represented as a structured MCP action with associated parameters.

Why is context important in factory robot NLU datasets?

Context determines whether identical commands should trigger different robotic behaviors under changing production conditions. Incorporating contextual attributes improves action selection accuracy.

How does command disambiguation contribute to reliable robot execution?

Command disambiguation reduces uncertainty in interpreting operator instructions and minimizes execution errors. It improves robustness when commands are incomplete, ambiguous, or dependent on environmental conditions.