Physical AI refers to AI systems that perceive and act in the physical world, such as robots and other embodied machines. Their infrastructure must connect sensor data, development, simulation, evaluation and deployment. Unlike a text-only application, a failure can affect equipment, people or the surrounding environment.
That makes the data path part of the engineering problem. A model result is difficult to interpret if the team cannot identify the sensor calibration, timing, environment and software version behind it.
What makes physical AI different from a chatbot?
A chatbot primarily produces information for a user to interpret. A physical system may use model output as part of a chain that moves an actuator or changes a machine's behaviour. The consequences and timing requirements can therefore be different.
Physical systems also observe the world through imperfect sensors. Cameras, depth sensors, joint states and other signals may arrive at different rates or contain gaps. A data pipeline must preserve enough context to explain what the system could actually observe.
A language model can still be part of a physical AI application, but it is only one component. Perception, planning, control and safety mechanisms need their own design and evaluation.
What data needs to be preserved?
Start with the raw observation and the metadata required to interpret it. Depending on the system, that may include timestamps, sensor identity, calibration, coordinate frames, device state and software versions.
Preserve the relationship between recordings and derived datasets. A labelled image, a selected trajectory and a simulation asset should point back to their origin. Otherwise, teams can spend time investigating a model failure that was actually caused by a data-preparation error.
Record permissions and retention requirements too. Physical-world recordings may contain people, locations or commercially sensitive operations. A technically useful dataset still needs an approved purpose and access model.
What does the infrastructure loop look like?
Each transition should create a record of what changed. The team should be able to trace a deployed model back to the data and evaluation that supported its release.
This diagram is a development loop, not a safety architecture. Independent safety controls and the relevant engineering assurance process must be designed for the actual machine and environment.
Where do simulation tools fit?
Simulation lets teams explore scenarios and evaluate behaviour under controlled conditions. It can be useful for development, regression checking and generating selected kinds of training material.
NVIDIA's robotics platform includes technologies relevant to this work, including Isaac-related tools. The right combination depends on the robot, task, assets and integration requirements.
A simulation result remains evidence about the simulated conditions. Differences in sensors, contact, lighting, latency or the environment can affect transfer to the real world. Keep those assumptions visible when interpreting the result.
What role do data and labelling providers play?
Scale AI's physical AI offering illustrates the role of data services in this category. A provider may help with parts of data collection or preparation, but the system owner still needs to understand provenance, quality and permitted use.
Define the labelling task precisely. A label useful for object detection may be insufficient for planning or manipulation. Review ambiguous cases and establish how corrections propagate to derived datasets.
Keep a quality process for labels just as you would for model outputs. A large dataset can contain systematic mistakes that a random glance will not reveal.
How should storage be organised?
Separate large immutable recordings from metadata used to find and interpret them. The storage design should support retrieval by task, device, time and relevant conditions without requiring researchers to browse opaque folders.
Keep dataset versions explicit. A training run should identify the selected records and transformations rather than referring loosely to “the latest data.” Record exclusions so the team can explain why a difficult scenario was omitted.
Plan for retention and retrieval costs. Physical data can accumulate quickly, and repeatedly moving it between environments may become an operational bottleneck. Measure the actual workload before choosing a storage or network design.
How do compute and orchestration change?
The platform may need to run data preparation, training, simulation and inference evaluation with different resource patterns. Some work is batch-oriented; some needs interactive access; some may require specialised acceleration.
Schedule those classes deliberately. A long simulation job should not unexpectedly consume resources reserved for a release evaluation. Record resource requirements and failure behaviour so jobs can be retried without losing their provenance.
A framework such as PyTorch may be part of model development, while orchestration and serving tools handle other responsibilities. Avoid treating a machine-learning framework as the entire operating platform.
What should evaluation include?
Evaluate the task, the environment and the relevant failure modes. Average performance alone can hide a rare but serious failure. Choose scenarios that reflect the intended operating conditions and the limits of the system.
Keep evaluation data separate from development where the comparison requires it. If the team continually tunes against the same scenarios, it needs additional evidence before claiming generalisation.
Record the full configuration: model, software, sensor assumptions, simulation assets and evaluation criteria. A result without those details is difficult to reproduce or compare after an update.
Physical deployment requires the appropriate safety and engineering review. A successful model benchmark or recorded demonstration does not establish functional safety.
Where does HyperLake fit?
HyperLake's physical AI direction connects customer-controlled data infrastructure with simulation, orchestration and model workflows. Components such as NVIDIA OSMO, Omniverse, Isaac Sim and Isaac Lab may be relevant, subject to licensing and validated integration.
The data foundation can include modular storage, query and governance components in the customer's environment. The useful design question is how a recording becomes a governed dataset and then an evaluated release.
HyperLake's public demonstration context includes recorded robot feeds and safety review. That should not be described as proof that an autonomous physical deployment is certified or safe for unrestricted operation.
What should a first project look like?
Choose a bounded task with available data and a clear evaluation environment. Establish ingestion, provenance and a reproducible evaluation before expanding the model or hardware scope.
For example, a team might begin by organising recorded sensor data and reproducing an existing evaluation. This can reveal timestamp, calibration and dataset-version problems before they become embedded in a larger programme.
Give the project a concrete handoff: a documented dataset, an evaluated artifact and an operating process another team member can repeat. That is a stronger foundation than a demonstration whose conditions cannot be reconstructed.
Frequently asked questions
Is physical AI the same as robotics?
Robotics is a major application area, but physical AI is a broader description of AI that perceives and acts in the physical world. The exact system and operating environment determine the engineering requirements.
Does simulation replace real-world evaluation?
No. Simulation provides evidence under modelled conditions. Real-world behaviour and the appropriate safety process still need assessment for the intended deployment.
What is the most important data requirement?
Preserve the context needed to interpret observations: provenance, timing, calibration, relevant state and software versions. Without that context, model results can be misleading.
Should teams buy GPUs first?
Start with the workload, data and evaluation requirements. Those determine compute, storage and network needs. Hardware alone does not create a repeatable development process.
