Physical AI at the Edge

If you spend enough time around industrial tech, you start to develop a filter for buzzwords. We’ve all finally settled into what "the edge" means for our infrastructure, and now a new term is starting to pop up in every keynote and LinkedIn feed: Physical AI.

It is easy to roll your eyes and assume it’s just marketing shorthand for robotics, but there is a real distinction here that matters for anyone building hardware today. Recent breakthroughs in artificial intelligence have moved beyond the screen. Understanding the interplay between edge AI and physical AI is the difference between a system that simply records a problem and one that actually fixes it in the physical world.

Let's take a look at the definition, hardware elements, and real-world applications of physical AI. But first, we need to define one more highly relevant term.

What is edge AI?

If you’ve ever tried to have a conversation over a video call with a 3-second lag, you have experienced a key problem edge AI was built to solve. In the industrial world, that latency is not just annoying; it is a process dealbreaker (more on that here).

Edge AI is essentially the move from "cloud-first" to "local-first" intelligence. Instead of shipping massive amounts of raw sensor data to a remote data center for processing, you are running your machine learning models and AI models right on the factory floor (or inside a vehicle, at a remote site, or in any other location where edge AI is being implemented). By placing computer hardware right where the data is being generated, you are giving the machine the ability to "see" and "interpret" its surroundings in real time.

By processing data at the source, edge AI handles the "triple threat" of industrial headaches: latency, bandwidth, and privacy. It utilizes sophisticated algorithms to answer specific questions, such as:

• Is there a crack in this turbine blade?

• Is that a person or a forklift in the camera frame?

• Is this motor vibrating at a frequency that suggests an imminent failure?

Unlike language models like ChatGPT, which act as digital AI agents responding to natural language, edge AI focuses on sensory perception, a vital element of current innovations in the automotive and industrial sectors. In these cases, the output is usually data: You get a notification, a log entry, or a red light on a dashboard. It is smart, but it remains a form of traditional AI because the output is digital information rather than physical action.

Read More: 3 Predictions for Industrial Edge AI

Moving into the real world: What is physical AI?

If edge AI is the brain perceiving the world, physical AI is the brain and the body working together in a closed loop. You might hear people call this "Embodied AI." It's what happens when you take that digital insight and turn it into action.

This transition is fueled by the rise of agentic AI. Unlike traditional software that follows a rigid, linear script, agentic AI systems are designed to achieve a specific goal by navigating complex, changing environments autonomously. In the physical world, this means a robot doesn't just "replay" a recorded motion; it uses agentic intelligence to figure out the best way to move a pallet or harvest a crop based on the obstacles it encounters in real time.

Physical AI takes the inference from the edge and translates it into a physical function. This is the world of autonomous mobile robots (AMRs), collaborative robotic arms, and complex autonomous systems. While edge AI might identify an obstacle in a warehouse, physical AI is the intelligence that calculates the exact torque, trajectory, and speed needed to steer around it without clipping a pallet. Implementing these physical AI systems requires a shift from pure data processing to embodied intelligence.

Edge AI vs. Physical AI: Understanding the closed loop

While the terms are often used interchangeably, the difference lies in where the process ends. At OnLogic, we see customers working across both spectrums, and understanding which one you are building for will determine your hardware and I/O requirements.

Edge AI is primarily about perception. It is an "open loop" system where the computer takes in data, processes it, and provides a digital output.

• The digital output: We often support edge AI applications where the result is a predictive maintenance alert on a dashboard or a security notification. That is smart, but it is purely digital.

• Example: A smart camera on a production line identifies a defective part and logs it in a database. The AI "saw" the problem, but a human must intervene to move the part.

Physical AI is about actuation. It is a "closed loop" system, often referred to as the "Sense-Think-Act" loop. The computer doesn't just identify a state; it calculates and executes a physical response in real time.

• The physical output: Physical AI completes the circuit by taking that inference and translating it into a physical function—like torque, trajectory, or speed.

• Example: An autonomous mobile robot (AMR) identifies a person walking across its path. The AI doesn't just log "human detected." It calculates a new path, adjusts the motor torque to decelerate, and steers the robot safely around the obstacle.

The role of simulation and digital twins

One of the biggest hurdles in developing physical AI is data collection. Training a robot in the real world is slow and expensive. This is where simulation and digital twin technology become essential.

Developers use high-fidelity simulation environments to create synthetic data. This data generation allows for reinforcement learning, where an AI model can practice a task millions of times in a virtual space before ever touching a physical component. By leveraging foundation models trained on open-source datasets, developers can improve the adaptability of these systems in complex environments.

By creating a digital twin of a factory floor, engineers can perform optimization on workflows without stopping production. This use of generative AI for robotics ensures that when the software is deployed to physical robots, they already possess the "experience" needed to operate safely, enabling more efficient optimization.

The technical engine: Accelerating the loop

To move from seeing to doing, the hardware has to be fast. You can't abide a loading icon when a robotic arm is swinging near a human coworker. Real-time decision-making requires carefully considered hardware to balance power needs and scalability.

Right-sized processing and NPU efficiency

While vision-heavy physical AI tasks may utilize platforms like NVIDIA® Jetson™ for massive parallel processing, a high-performance discrete GPU is not always the most efficient choice for volume deployment. Modern System on a Chip (SoC) architectures from chip providers like Intel®, AMD, and Qualcomm now integrate powerful Neural Processing Units (NPUs) and integrated graphics (iGPU) directly into the processor to handle complex AI and graphical workloads efficiently on a single chip.

For many AI-driven devices, this integrated approach is more cost-effective and energy-efficient, providing the instant responsiveness needed for the "Sense-Think-Act" loop while lowering the total cost of ownership (TCO). By using tools like the Intel OpenVINO™, developers can spread workloads across the Central Processing Unit (CPU), iGPU, and NPU, ensuring a "right-fit" solution that avoids the high power consumption and stability challenges often associated with more complex discrete setups.

Read More: NPUs vs GPUs for Edge AI

Real-world applications of physical AI

This isn't a theoretical concept. The use cases for physical AI run the gamut from making minor improvements to processes, to revolutionizing entire industries. Our customers are already using these technologies across various industries, including:

• Smart warehousing: While self-driving cars are a well-known example of physical AI in the automotive sector, the technology is also making waves in global supply chains. For instance, Amazon has deployed fully autonomous mobile robots (AMRs) like Proteus to navigate around employees safely, and organizations are experimenting with humanoid robots for some repetitive tasks. These physical AI systems use advanced computer vision to handle the unpredictability of a busy warehouse, leading to the optimization of fulfillment workflows.

• Autonomous agriculture: Bear Flag Robotics (now a John Deere company) uses ruggedized edge computers to turn standard tractors into autonomous vehicles. The system processes LiDAR and sensor data locally to identify uneven soil or obstacles, immediately adjusting steering and braking to keep tilling precise in dusty, high-vibration environments.

• Healthcare: From surgical assistance to autonomous delivery carts, physical AI is improving patient outcomes. In hospital settings, these systems handle precise movements and logistics, ensuring that life-saving supplies reach their destination without being delayed by crowded hallways or complex floor plans.

• Infrastructure inspection: Autonomous robotic systems are being deployed to inspect bridges, pipelines, and power lines. These robots navigate complex environments to collect high-fidelity sensor data and use computer vision to identify structural issues. These physical AI systems can even perform minor repairs in locations too dangerous or remote for human crews.

How the industrial edge makes it possible

You cannot have physical AI without a rock-solid foundation. For a machine to interact with the world safely, the "Sense-Think-Act" loop has to happen in milliseconds. Industrial edge computing makes this possible by providing:

• Deterministic performance: Unlike a standard PC, industrial hardware is built for high-speed I/O. This ensures that when the AI says "stop," the command reaches the actuators instantly.

• Environmental resilience: Physical AI lives on vibrating factory floors and in dusty fields. Our fanless, rugged systems make sure the "brain" does not overheat or shake loose when things get tough.

• Deep connectivity: These systems act as a bridge, connecting neural networks to the sensors (LiDAR, cameras) and actuators (motors, hydraulics) that make movement happen.

Purpose-built for the next frontier

At OnLogic, we're laser-focused on helping our customers "Make it Possible." Whether you're building a simple monitoring solution or a complex autonomous robot, the hardware should be the least of your worries.

By providing highly configurable, modular systems, we make sure you have the exact power and connectivity you need to take your AI out of the cloud and put it to work in the real world. Physical AI is the next frontier of automation, and we are ready to help you build it.