Physical AI depends on more than robots, sensors, and advanced AI models. Real-world and synthetic robot experiences must be structured, annotated, connected to human instructions, validated, and turned into reliable training and evaluation data. This article looks at the data work behind that process and where multilingual and human-in-the-loop operations fit in.
Physical AI is moving robots beyond fixed, pre-programmed tasks. The goal is to enable machines to perceive their surroundings, understand instructions, make decisions, and act in changing real-world environments.
That shift is also changing the role of data.
Recent robotics development increasingly treats data generation, curation, simulation, model training, and evaluation as parts of one connected pipeline. NVIDIA, for example, introduced its Physical AI Data Factory Blueprint in 2026 to support large-scale data processing and curation, synthetic data generation, reinforcement learning, and model evaluation for robotics and autonomous systems. Google DeepMind’s Gemini Robotics 2 similarly reflects the move toward models that connect multimodal understanding with real-world robotic action.
As physical AI systems become more capable, a practical question follows:
What kind of data work is required to train and evaluate them?
Physical AI Learns from What Happens in the Real World
Large language models learned mainly from text. Physical AI has to learn from what happens when machines interact with people, objects, and physical environments.
Consider a simple instruction:
“Pick up the bottle on the table and place it on the shelf.”
A robot has to locate the bottle and shelf, move its arm toward the correct object, grasp it with enough force, lift it, move through the workspace, and place it safely. If the bottle slips or someone crosses its path, the robot may also need to adjust its behavior.
One short task can therefore produce several kinds of data at the same time: camera images, the user’s instruction, the position of objects, robot joint states, movement trajectories, grasp information, timestamps, and the final result.
A complete record of one task is often treated as an episode or trajectory. Large numbers of these experiences are used to help robots learn how actions relate to different objects, environments, and outcomes.
But collecting those experiences is only the beginning.
Raw Robot Data Still Has to Be Made Usable
A 15-second recording of a robot moving a bottle may look straightforward to a person. For AI training, the sequence may need to be divided into meaningful steps:
approach → reach → grasp → lift → move → place → release
The dataset may also need to identify which object was involved, when each action started and ended, whether the task succeeded, and where a failure occurred.
This requires data structure design and annotation.
Before annotation begins at scale, someone has to decide what counts as a task, how far an action should be broken down, what terminology should be used for each action, and how success and failure should be classified.
A simple structure might be:
Task → Action → Object → Result
A more detailed project might add subtasks, timestamps, environmental conditions, failure types, or recovery actions.
Without shared rules, one annotator may call an action “pick up,” another “grasp,” and another “lift,” even when they are looking at the same behavior. Large datasets can quickly become inconsistent.
This is not entirely new to AI data work.
In one multilingual AI training data project, Hansem Global first analyzed representative source documents and created a reusable schema before large-scale annotation began. The project covered four languages and two regulatory regions, with shared naming rules, field definitions, annotation criteria, and quality controls applied across tens of thousands of document pages.
The subject matter was document data rather than robot behavior, but the underlying problem was similar: define the structure first, establish shared annotation rules, apply them consistently at scale, and validate the output.
In a physical AI project, those same operating principles could extend to areas such as robot action annotation, task and action taxonomy design, trajectory segmentation, and physical AI dataset validation.
Synthetic Data Is Becoming Part of the Same Pipeline
Real-world robot data is valuable, but collecting enough of it can be slow and expensive. Some situations are also difficult or unsafe to reproduce repeatedly.
This is why synthetic data and simulation are becoming increasingly important in physical AI development.
A simulated environment can change object positions, lighting, backgrounds, obstacles, or operating conditions and generate many variations of a task without repeatedly setting up the same physical scene. World models can also be used to expand existing datasets and create scenarios that are difficult to capture in the real world.
NVIDIA describes synthetic data as an important way to address the cost and difficulty of collecting and labeling large amounts of real-world physical AI data. Its current robotics workflow combines real-world data, simulation, world models, synthetic data generation, and model validation.
This does not remove the need for data operations. It changes them.
Synthetic datasets still need to be checked for coverage, consistency, metadata quality, labeling accuracy, unrealistic scenarios, and gaps between simulated and real-world behavior. Teams also have to determine which variations are useful for training rather than simply generating more data.
In other words, data generation can become more automated while data design, curation, and validation remain important.
Language Is Also Part of Physical AI Data
Physical AI data is often associated with video, sensors, and movement. For robots that interact with people, language is another important layer.
People rarely give the same instruction in exactly the same way.
A user might say:
“Put the cup on the table.”
Another may say:
“Move that cup over to the table.”
Someone else might simply say:
“Can you put this over there?”
The intended action may be similar, but the wording, context, and level of detail are different.
This is particularly relevant to Vision-Language-Action (VLA) models. In simple terms, a VLA model connects what a robot sees, what a person says, and what the robot should do.
Once robots are deployed across markets, the language problem becomes larger. A robot trained mainly on English instructions may eventually need to understand natural commands in Korean, Japanese, German, Spanish, or many other languages.
This is not just a translation problem.
Users in different countries may refer to the same object differently, omit information that is obvious in context, use local expressions, or give instructions in more or less direct ways. Useful training and evaluation data may therefore require multilingual robot instructions, natural paraphrases, language-specific validation, and multilingual VLA evaluation.
Hansem Global has already worked with a related human-generated data model. In a recent AI evaluation project, the company recruited and trained native-language contributors across more than a dozen countries and languages to create original, culturally grounded evaluation prompts. Contributors tested their own prompts against the target model, and a separate QC team independently reviewed the prompt, answer, and model response before acceptance.
The project did not involve robots, but it demonstrated a reusable operating model:
distributed contributors → controlled data creation → model testing → independent review → reusable evaluation data
That approach could be adapted to future physical AI projects that require multilingual robot instruction data or language-based evaluation of robot behavior.
Model Evaluation Changes When AI Starts Acting
Evaluating a chatbot usually means examining its response. Evaluating physical AI can involve examining an action.
Suppose a robot receives the instruction:
“Place the blue bottle on the left side of the tray.”
The evaluation may need to determine whether the robot selected the correct object, understood “left,” placed the bottle in the correct location, completed the task safely, and recovered appropriately if something went wrong.
The same instruction can then be tested with different wording or in different languages.
This creates new types of evaluation data. A dataset may record the instruction, expected behavior, actual robot behavior, failure category, recovery behavior, and final outcome.
The quality task is no longer limited to asking, “Was the sentence correct?”
It may become:
Did the model understand the instruction correctly, and did that understanding result in the correct physical action?
This is where language evaluation, behavioral annotation, and model evaluation begin to overlap.
Scaling Physical AI Data Also Means Scaling People
Automation will handle more parts of the physical AI data pipeline over time. Even so, large data programs can involve substantial human participation.
Depending on the project, people may operate robots to demonstrate tasks, review video and trajectory data, annotate actions, write natural-language instructions, validate multilingual content, classify failures, or provide domain knowledge about manufacturing, logistics, healthcare, or other operating environments.
The challenge is not only finding enough people. It is finding the right people for the right task, confirming that they are available, assigning them correctly, and maintaining consistent standards as the project changes.
This is already a familiar problem in large-scale AI data programs.
Hansem RMS and Large-Scale Human-in-the-Loop Operations
Hansem Global developed Hansem RMS (Resource Management System) in response to this operational problem.
AI data projects can require many contributors within a short period, often with specific combinations of language, location, experience, or subject knowledge. Managing such projects through spreadsheets and individual email exchanges becomes increasingly difficult as the resource pool grows.
Hansem RMS was built to manage the workflow from resource registration and search through candidate selection, availability checks, project requests, assignment, and communication history.
It was not developed specifically for physical AI.
It was developed because large human-in-the-loop AI data projects created a recurring need to identify suitable contributors quickly, confirm who could actually participate, and keep assignment and communication status under control.
The same operational issue is likely to appear in larger physical AI data programs.
A project may need one group to annotate robot actions, another to create language instructions, separate reviewers to validate the data, and domain specialists to resolve cases that require knowledge of a particular industrial process.
At that scale, having a large resource pool is not enough. The ability to search, qualify, activate, assign, and track that resource pool becomes part of the data production infrastructure.
The Physical AI Data Supply Chain Is Broader Than Data Collection
Physical AI data is sometimes discussed mainly in terms of collecting robot demonstrations or sensor recordings. Those activities are essential, but they represent only one part of the overall workflow.
A more complete data pipeline may look like this:
real-world or synthetic data generation → data curation and structuring → task and action annotation → quality validation → instruction data creation → multilingual evaluation → model feedback → new data generation
Different suppliers may participate at different points.
Robotics specialists may operate robot fleets and capture sensor data. Simulation teams may create synthetic environments. AI data companies may structure and annotate trajectories. Language specialists may create or validate instructions. Domain experts may review task accuracy. QA teams may evaluate whether the resulting dataset is consistent enough for training.
As physical AI moves toward real-world deployment, these layers are likely to become more connected.
Hansem Global’s current experience sits in specific parts of this emerging supply chain rather than across the entire robotics stack. Its work in multilingual annotation, schema design, human-generated evaluation data, independent quality review, and large-scale resource operations provides a foundation for areas such as physical AI data annotation, multilingual robot instruction data, dataset QA, and multilingual model evaluation.
The underlying data is changing. Instead of only documents, text, and model responses, future datasets may increasingly contain objects, actions, trajectories, environments, and human instructions.
The operational question, however, remains familiar:
How do you turn large volumes of complex raw data into consistent, reliable data that an AI system can actually learn from?
That question will remain central as physical AI moves from research environments into factories, warehouses, vehicles, service environments, and eventually everyday life.