Physical AI & Robotics Services

Digital-native AI like LLMs operate entirely within virtual environments, processing assets and outputting answers into an open-loop interface with no feedback to verify and adjust its performance. Physical AI has to run several additional processes in a continuous loop, adjusting based on real-time feedback:

• Perception: Gathering a constant stream of raw data about the surrounding environment through cameras, LiDAR, radar, force-torque and tactile sensors.
• Reasoning: Interpreting the spatial and temporal context it’s perceived to plan its next action, using more complex vision-language-action (VLA) models and world models instead of a purely language-based model.
• Action: Executing the reasoned decision through motors, actuators, robotic joints or control surfaces, then observing the result and adjusting as required.

Physical AI can't be trained the same way a chatbot is trained: they need to employ closed loop training rather than a static dataset. A realistic, physics driven simulated environment allows the agent to act, observe the changes caused by its actions and adjust them across millions of continuous timesteps with no risk of damage or injury. 

• 3D and sensor annotation: Dense, multi-modal data annotation across camera, LiDAR point-cloud and sensor-fusion data, with high inter-annotator agreement and temporal continuity across frame occlusions.
• Sim2Real Evaluation: Independent, repeatable testing of your policy against real-world edge cases and anomalies, with scored results that show where it will fail before it reaches physical hardware.
• Motion and teleoperation capture: Our in-house Mocap Lab and Teleop Lab record human spatial movement in detail and map that onto robotic kinematics to produce clean state-action datasets for imitation learning in HDF5 or Parquet formats.
• Simulation environments and digital twins: Simulating high-fidelity, AAA-quality virtual worlds and digital twins built via our World Lab and Scenario Lab, with automated ground-truth labelling built in.
• State-action dataset curation and action libraries: Converting captured motion into the structured formats that your model needs as training data, ready to be fed into SFT, RLHF or DPO pipelines.
• Sim2Real adversarial testing: Our Scenario Lab and World Lab thoroughly test control policies with adversarial edge cases to define exactly where a model's stability breaks, long before it interacts with real hardware or people.
• Domain randomisation and edge-case generation: Headless, parallelised simulation runs that stress-test policies across thousands of randomised scenarios.
• Digital twins for vendor assessment: Construction of 1:1 digital twins of operational facilities, allowing you to import any vendor’s robot via URDF (Unified Robot Description Format) and evaluate physical fit, site throughput, and safety protocols without shutting down the line.
• Egocentric & video-based robot learning data: Turning first-person video footage into training-ready data for VLA models, segmented by subtask, labelled with trajectory over time and greyboxed into 3D scenes from the raw footage.
• Continuous policy regression: As facility layouts, SKUs, and edge cases evolve over time, so does your digital twin, becoming a permanent regression harness for every future policy update.

We support enterprise teams moving from proof-of-concept into live production deployment  across several core areas, including:

Robotics and Manipulation

Robotics is ‘embodied AI’ in the strictest sense: where the system's competence comes from physical interaction rather than fixed programming. Developing humanoid robots or robotic manipulators needs human demonstration data to build action libraries to train from. 

We capture expert trajectories within our Mocap Lab and Teleop Lab, then convert them into clean state-action datasets to be used in imitation learning.

Drones and Unmanned Aerial Systems (UAS)

Autonomous drones operate in unpredictable conditions where small variations in wind, payload and battery displacement can all affect flight control. We work with UAS manufacturers across four key areas:

• Perception training data.
  Synthetic Electro-optical (EO) and infrared (IR) datasets built from real GIS and elevation data, with configurable weather, time of day and seasons and pixel-accurate ground truth on every frame, optimized for the hardware your operators carry. We deliver material metadata for your own thermal shader or IR layer as well as annotation of your flight footage to keep labelling consistent.
• Autopilot and failure-mode testing.
  Headless, batch simulation that injects sensor loss, GPS degradation and wind gusts to find exactly where your flight stack breaks, validated against real flight data. Your aerodynamic and loads analysis stays with your specialists; we sit between that analysis and physical test.
• Operator training simulators.
  Game-quality, real-time training simulations, built by studios with 28 years of flight and military sim titles behind them, that run on your own controller hardware and ground station, so a new operator's first flight isn’t with your actual aircraft.
• Mission rehearsal.
  Lightweight rehearsal of a specific flight over real terrain to catch planning errors that a map view won’t show alone, including altitude versus height above ground, line-of-sight gaps and obstacle clearance.

We integrate with your existing stack (PX4 and ArduPilot, ROS 2 and Unreal-based tooling) and can export environments to whichever simulators you already run.

Autonomous Vehicles and Mobility

Self-driving platforms need to make sense of the world around them through cameras, LiDAR and radar, then react correctly to the unusual situations that most driving never encounters. We deliver detailed annotation across all of these data types, kept consistent by trained annotators and tracked accurately even when a vehicle briefly loses sight of something.

Logistics, Transportation and Warehousing

Warehouse operators and autonomous fleet developers face two distinct challenges: training robots to move through busy, fast-changing spaces, and evaluating new robotics vendors without collisions or unwanted actions that could grind operations to a halt. 

We build precise digital twins of your warehouse or facility, with ground-truth labelling built directly into the simulation. Autonomy teams use these environments to train AGVs and AMR fleets, while enterprise deployers import third-party robots via URDF to benchmark site fit, traffic flow, and throughput before committing to live equipment purchases. 

Manufacturing and Industrial Automation

When industrial machinery or automated humanoids get a decision wrong, the result can be mechanical failure, costly downtime, or a human safety risk. For Original Equipment Manufacturers (OEM), we build accurate, physics-based simulations for rigorous safety testing, then stress-test your controllers against rare and difficult edge cases long before they reach the factory floor. 

Manufacturers, utilities, food producers and trading houses looking to further automate their lines face a different question: which robot fits their site, and how do they evaluate vendors without shutting down the line for a live trial. To help you assess vendors without the disruption, we can create 1:1 vendor-neutral digital twins of operational facilities to assess how well each vendor fits, without committing to a pilot or a purchase.

AgTech

When autonomous tractors, robotic harvesters, or field sensors fail due to software bugs or miscalibration, the result is severe crop loss, expensive equipment damage, and missed harvesting windows. Testing new machinery or agronomic strategies in live fields is slow, seasonal, and high-risk. 

We build physics-based, scenario-driven simulation environments to stress-test field robotics and control algorithms against unpredictable environmental conditions long before equipment reaches the dirt. For autonomy teams, we provide synthetic sensor datasets and precise annotation to keep vision systems reliable across changing light, weather, soil, and crop varieties.

Healthcare

Deploying physical AI alongside patients and clinical staff, whether home-care humanoids, robotic surgical assistants, or autonomous hospital logistics, leaves zero margin for error in navigation or physical contact. 

To help robotics teams validate safety long before live deployment, we build high-fidelity digital twins of clinical facilities and care environments to stress-test collision avoidance, haptic manipulation, and edge-case protocols in unconstrained spaces. To accelerate policy learning, we capture expert physical demonstration data within our Mocap and Teleop Labs, converting real human caregiving and mobility trajectories into clean action libraries.

What is Physical AI?

Physical AI refers to AI systems with a physical presence in the real world, such as robotics, drones, autonomous vehicles or industrial machinery. Physical AI systems need to perceive the environment around them, then reason and act on that data live within a three-dimensional environment, so have several distinct challenges when compared to their on-screen counterparts.

What is the Sim2Real gap?

The Sim2Real gap refers to the difference between how a policy performs inside a simulation, which often presents an idealised environment, versus how it performs on real hardware. Real-life environments come with a number of variables that are hard to accurately train for, such as wind-speed, load-shift and variations in surface friction (among many others). Closing the Sim2Real gap requires simulated environments which can provide realistic randomisation of these variables to train against, rather than a single ideal.

Can simulation fully replace real-world data collection?

Simulation is essential for scale and for edge cases that are unsafe or rare to capture in the real world, but policies trained purely in simulation are much more prone to the Sim2Real gap, failing once they’re deployed in a physical environment. The most reliable programs combine simulation with real-world demonstration and sensor data.

What's the difference between Physical AI and embodied AI?

Physical AI is the broad term for an AI system that acts in the physical world, whilst embodied AI is a more specific subset of Physical AI that learns and improves through physical interaction with its environment via a body, rather than being programmed with fixed rules. An example of embodied AI would be a robot which can adjust its own balance through trial and error to adapt to encountered conditions. 

Does Keywords Studios work with our existing tech stack?

Yes, our simulation and digital twin work is engine agnostic, so will work within Isaac Sim, Omniverse, Unreal Engine and custom physics backends, and we integrate directly into your existing development environment, including ROS 2, PX4, ArduPilot and custom pipelines, so there’s no need to switch tools to work with us.

Does Keywords Studios build or own the robot policy?

No, Keywords Studios builds the simulation environments, data pipelines and evaluation harnesses that your policy needs. We don’t build, train or own your policies or AI models: all data and IP stays 100% yours.

How much do simulation and data capture programs cost?

Our programs are structured on a pilot-first basis, scoped by scenario complexity, target fidelity requirements, and data capture volume. Contact our team to discuss your project for an accurate quote.