Sensor technology: the foundation of physical AI
AI robots, humanoids, AGVs, digital twins and self-optimizing systems are just a few of many applications that would be inconceivable without the impressive advances in sensor technology. So it isn’t much of a surprise that the advancement of AI greatly depends on the advancement of sensor technology.
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For more information on Physical AI and its potential for manufacturing, see the article “How physical AI unlocks next-level production ”
The dynamic development in this area was already evident at automatica 2025. Plants, robots, and humanoids taking automation to new levels with physical AI were real crowd-pullers. The associated machines are designed to mimic humans: perceive, interpret, react. High-precision sensors act as sensory organs in this context. They are the foundation of every physical AI system and play a key role in making robots precise, flexible, and reliable. Visual sensors for applications such as cameras, 3D machine vision systems, and LiDAR enable robots to navigate their environment and recognize objects in real time. They represent the most commonly used type of sensors. Companies like SICK, Cognex, SensoPart, ifm, and many more now specialize in this field.
Torque, force, and position sensors give the robot what we commonly call dexterity. They provide feedback on acting forces and contact properties. Acoustic and thermal sensors complete the picture.
Anja Schneider, automatica Exhibition Director, comments: “The significance of sensor technology for AI-based automation is also reflected in the steadily rising number of exhibitors from this industry. As more and more sensor manufacturers from across sectors exhibit at automatica in Munich, visitors can get a comprehensive overview of new technologies including sensor data fusion, which is particularly relevant for AI processes.”
How sensor technology and compute turn into AI skills
Sensors capture environmental data used by data fusion to generate a consistent representation of the situation, which is then interpreted and translated into motions by an AI model. This process is based on an AI skill: a trained ability the robot can apply to new but similar situations, enabling it to understand any relevant interdependencies.
Such models are trained with reinforcement learning (a process involving the use of trial and error for the robot to learn which motions lead to the desired outcome) or imitation learning (a process in which the robot learns by observing human actions). Both methods require large amounts of high-quality training data and virtual simulation environments where millions of scenarios can be acted out before a robot physically grasps an object for the first time.
Text: Ralf Högel