Most of the recent excitement around artificial intelligence (AI) has focused on systems that live entirely in the digital world: large language models (LLMs) generate text, image classifiers label photos, and commendation algorithms suggest movies or products. A growing area of research and industry development is pushing AI out of the cloud and into machines that interact with the real world. This trend is often referred to as physical AI.
At a high level, physical AI describes systems where artificial intelligence is used to perceive the environment, make decisions, and produce actions that affect the physical world. Instead of simply processing information, these systems sense, reason, and act through real hardware. If that sounds similar to robotics, you’re not wrong. Physical AI overlaps heavily with robotics, but the term highlights an important shift in how intelligent systems are being designed and deployed.
Understanding this shift is especially important for engineers working in robotics, embedded systems, and edge AI. Let’s take some time to discuss this growing trend.
From Digital AI to Physical Systems
Most modern AI systems follow a fairly simple pattern: they take in some data, process it with a model, and produce an output. For example, a neural network might take an image and output a classification label, or a language model might take a prompt and generate text. This entire process happens in software. Even when these models operate in real-time applications, the output remains digital.
While this works well for LLM-based chatbots and automatic image classification systems, many new and innovative AI systems need to translate those predictions into actions. This is especially true for edge AI.
Consider a robot navigating a hallway. The system might use cameras or LiDAR to observe its surroundings. A machine learning model processes that sensor data and identifies obstacles or free space. A control algorithm then converts that information into motor commands that steer the robot.
Another example might be a smart building climate system. Sensors around the building measure temperature, humidity, and occupancy. A machine learning model analyzes these inputs and predicts how conditions will change over time. Instead of simply reporting this information, the system adjusts HVAC controls (e.g. opening vents, changing fan speeds, or activating cooling systems) to maintain comfort while minimizing energy usage.
In both cases, the AI system is part of a continuous feedback loop between sensing, reasoning, and acting. This is the essence of physical AI: instead of producing answers for humans, the system produces decisions that influence the physical world.
With physical AI, we’re moving beyond just interpreting the data and into the realm of systems taking action in the real world.
Physical AI and Robotics
The connection between physical AI and robotics is obvious, but the two terms come from slightly different traditions.
Robotics has historically focused on building machines that move and interact with their environment. Engineers in this field study mechanical design, kinematics, control theory, and motion planning. For many years, robotics systems relied primarily on deterministic algorithms rather than machine learning. A warehouse robot, for example, might have used rule-based navigation and simple sensor thresholds to avoid obstacles. These systems worked well in structured environments where everything was predictable.
Over the past decade, however, machine learning has started playing a larger role in robotics. AI models now help robots recognize objects, understand scenes, plan movements, and adapt to changing conditions. As a result, the intelligence driving these machines is increasingly learned rather than hand-programmed.
Physical AI is essentially what happens when robotics systems incorporate modern AI techniques as a core part of their perception and decision-making stack. In simple terms, robotics provides the machines, while physical AI refers to the intelligence that allows those machines to operate autonomously.
Embodied AI and Embodied Intelligence
When reading about physical AI, you’ll often encounter two closely related terms: embodied AI and embodied intelligence. Although the terminology can vary slightly depending on the context, the ideas are closely related.
Embodied AI usually refers to AI systems that operate within a body (a physical device capable of sensing and interacting with the world through sensors and actuators).
Embodied intelligence is a broader concept that suggests intelligence emerges through the interaction between a body and its environment. In other words, intelligence is not just a property of an algorithm, rather it arises from the combination of perception, action, and experience.
These ideas are inspired partly by how humans and animals learn. Much of our understanding of the world comes from interacting with it physically. Infants explore objects, test boundaries, and gradually build internal models of how things behave.
Researchers exploring embodied AI are interested in building machines that learn in a similar way. Instead of relying only on labeled datasets, these systems learn through trial and error, exploration, and interaction. This approach is closely related to techniques like reinforcement learning (RL), where an agent learns by taking actions and receiving feedback from the environment.
Where Embedded Systems Fit In
For engineers working in embedded systems, physical AI is particularly relevant because these systems ultimately run on hardware devices. The physical world doesn’t operate inside a data center. Robots, drones, industrial machines, and smart appliances all rely on embedded processors to perform their computations, and they often require the latency and reliability afforded by running AI locally (i.e. edge AI).
A typical physical AI system might include sensors such as cameras, IMUs, or microphones connected to an embedded processor. The processor runs machine learning models to interpret sensor data and determine what actions to take. The resulting decisions are then translated into commands for actuators like motors, servos, or steering systems.
This entire process often happens under strict constraints. Embedded platforms may have limited memory, limited compute power, and tight power budgets. In many cases the system must also meet real-time deadlines. These constraints force engineers to carefully optimize their models and software pipelines. Techniques like model quantization, operator fusion, and hardware acceleration become essential for making AI practical on embedded devices.
This is why physical AI is closely tied to edge AI, where machine learning models run directly on local hardware rather than in the cloud. However, the two ideas are not identical. Edge AI refers to where the computation happens (on-device rather than in the cloud) while physical AI refers to systems where AI decisions influence behavior in the physical world. Many physical AI systems rely on edge AI to meet real-time and reliability requirements, but not all edge AI systems interact directly with the physical environment.
The Sim-to-Real Problem
One of the major challenges in physical AI is something known as the simulation-to-real (Sim2Real) gap.
Training intelligent systems in the real world can be slow and expensive. Robots may break during experimentation, and collecting large datasets from physical systems can take significant time. To speed up development, researchers often train models in simulation environments. Modern robotics simulators can replicate physics, sensors, and complex environments, allowing engineers to run thousands of training iterations much faster than would be possible in the real world.
However, simulated environments are never perfect replicas of reality. Sensors behave differently, lighting conditions change, and real surfaces may not behave exactly as the physics engine predicts. As a result, models that perform perfectly in simulation may struggle when deployed on real hardware. Bridging this gap between simulated and real environments remains an active area of research.
To address this problem, researchers often use techniques like domain randomization, where simulations deliberately vary lighting, textures, sensor noise, and other environmental factors during training. The idea is to expose the model to enough variation that it learns behaviors that generalize to the real world. Even with these techniques, however, sim-to-real transfer remains a difficult challenge in robotics and physical AI.
The Physical AI Trend
Several technological trends are making physical AI increasingly practical. Sensors have become dramatically cheaper and more capable over the past decade. Cameras, IMUs, and depth sensors are now small enough and inexpensive enough to integrate into everything from drones to consumer electronics. At the same time, specialized hardware for machine learning (such as NPUs, AI accelerators, and embedded GPUs) makes it possible to run neural networks directly on embedded devices rather than relying on the cloud.
Advances in machine learning algorithms and simulation tools have also accelerated development. Robotics simulators and modern training techniques allow engineers to experiment, train models, and test behaviors far more quickly than was possible in the past. Together, these developments are enabling systems that can perceive their surroundings, make decisions, and interact with the physical world in increasingly sophisticated ways.
For embedded engineers, this represents a convergence of several traditionally separate disciplines. Building intelligent physical systems often requires knowledge of sensors, embedded software, control systems, and machine learning. As a result, engineers who understand how these pieces fit together are becoming increasingly valuable in industries ranging from robotics and autonomous vehicles to industrial automation and smart consumer devices. In many ways, physical AI represents the next step in the evolution of computing, from machines that simply process information to machines that actively interact with the world around them.