The Technology Behind Physical AI

You may have started to hear the term ‘Physical AI’ recently, as the buzz around digital AI is now extending to practical applications. But what does it mean? The term refers to AI systems that have a physical vessel, allowing them to interact with the real world. That might sound futuristic or scary, but a lot of these systems build on technology that already exists or has been in development for some time.  Physical AI can take the form of robots, drones, autonomous cars or even humanoid robots.

In an industrial setting, this would allow a robot to adapt to and learn from unpredictable surroundings, rather than simply following a pre-programmed script. This could involve picking up randomly positioned or new, irregularly shaped objects – making automation more adaptable to changing production needs – or operating on moving production lines. Similarly, Physical AI enables robots to respond to hazards in real time.

So, what is the technology behind Physical AI?

There are multiple physical and digital technologies that have enabled the advancement of robotics into the space of Physical AI, from sensors and visions systems to open-platform software for AI programming. We are breaking it down into the 7 key areas.

1. Advanced Perception & Sensor Systems

Physical AI relies on robots being able to sense their environment with high fidelity.

Key components include:

• 3D vision systems e.g. like the robot vision available from Cambrian Robotics and Inbolt.

• LIDAR & radar

• Force/torque sensors for touch

• Proximity sensors

• IMUs* for balance and movement

*IMUs = Inertial Measurement Units. These are electronic devices that measure and report an object’s force, angular rate and orientation using a combination of sensors.

These allow robots to perceive complex, moving environments in real time.

Inbolt's Real-time Robot Guidance.

2. Physics‑Aware Simulation

Before a robot can move safely in the real world, it must learn physical laws such as gravity, friction and collision dynamics. Robots learn these principles virtually through reinforcement learning and imitation learning in simulation environments, mastering tasks before deployment.

Core technologies include:

• Physics-based simulation engines

• Neural graphics and synthetic data generation

• Large‑scale reinforcement learning environments

3. Robotic Mobility & Actuation

There is a need for physical hardware that can move with precision and adaptability.

This includes:

• Robotic arms with multi‑axis joints

• Humanoid or wheeled robotic platforms

• AMRs (autonomous mobile robots)

• Driverless cars

• Drones

UR Collaborative Robots.

4. On‑device AI Compute

Physical AI requires real‑time decision-making on the robot, not in the cloud.

Key enabling hardware:

• Embedded GPUs and edge AI processors

• Low‑latency compute modules

• AI accelerators

NVIDIA and others are driving this shift by enabling AI models that understand physics to run on physical systems like robots and vehicles. The AI Accelerator from Universal Robots combines an embedded NVIDIA Jetson Orin AGX 64GB compute box with 3D vision to add AI to robotic solutions.

Universal Robots' AI Accelerator.

5. Predictive & Adaptive Control Maths

The biggest leap isn’t hardware – a lot of this already exists – it is the mathematics behind control systems.

Emerging techniques powering Physical AI include:

• Dual numbers and Jet calculus for modelling change

• Dynamic optimisation

• Scenario prediction (“what‑if” motion planning)

• Adaptive control

This allows robots to anticipate outcomes, not just react, enabling safe real-world behaviour.

6. Imitation Learning & Human-Robot Collaboration

Following on from simulation learning, robots can also learn from observation and collaboration.

• Robots learning by watching human demonstrations

• Shared behaviour models across robot fleets

• Peer‑to‑peer learning (robots learning from each other)

  
  

These are all other methods of gaining data for Physical AI training, which are followed by real-world training.

7. Autonomous Mobility & Navigation Systems

Physical AI extends to robotic systems that can move autonomously, such as:

• Autonomous vehicles

• Humanoids

• Warehouse AMRs

• Drones

MiR Pallet Jack.

These rely on:

• SLAM (Simultaneous Localisation and Mapping)

• Multi-sensor fusion

• Real-time path-planning

These technologies enable robots to navigate safely without bumping into obstacles or hazards.

What does this mean for manufacturers?

The reality is that Physical AI is still in its early stages. Large language models (LLMs) like ChatGPT were trained on the vast amount of data available on the internet. The same is needed when training physical AI, but with multimodal physical data: through simulations, observation training and real-world trials.

Robotiq's Tactile Sensor Fingertips for Physical AI Training.

The technology that provides the foundation for Physical AI systems in the future has been around for a while, such as AI-driven 3D vision systems that are largely adopted in the market already, like Inbolt and Cambrian Vision. AMRs from Mobile Industrial Robots have built in LIDAR scanners that allow them to navigate autonomously. MiR’s Pallet Jack also has an NVIDIA-powered AI pallet detection functionality.

Currently, the pace of development means it’s important for manufacturers to stay informed and realistic. The opportunities are significant, particularly around productivity, safety and flexibility, but there are also genuine considerations around skills, integration and responsible deployment. Physical AI isn’t an overnight revolution, but it promises to shape the next phase of industrial automation as the technology matures. Watch this space…