Humanoid robots are robots with a human-like physique designed to move through environments and use tools built for humans. In logistics and production, they currently work almost exclusively in pilot projects with small fleets and clearly defined tasks.
Humanoid robots are robots with a human-like physique designed to navigate environments and use tools built for humans. In logistics and manufacturing, they are currently used almost exclusively in pilot projects involving small fleets and clearly defined tasks.
Humanoid robots feature a torso, a head, two arms with hands, and usually two legs. The goal is not visual resemblance, but the ability to operate in existing work environments. In addition to bipedal models, there are variants with a human-like upper body mounted on a mobile base. These are more energy-efficient and stable; both designs are currently being tested in industrial settings. Humanoid robots represent the most demanding application of Physical AI, as they integrate all layers of the Physical AI stack into a single body.
Factories and warehouses are built for humans. Stairs, shelf heights, door handles, and tools are all designed for the human body. Theoretically, a humanoid robot can therefore integrate into existing workflows without the need to modify the facility. Furthermore, there is the factor of versatility. While a classic industrial robot is optimized for a single task, a humanoid robot is intended to perform many different activities using the same hardware, depending on what its software has learned. They are also easier to teach by observing humans: because their physique and movements are similar, human demonstrations and motion data can be more easily transferred to the robot. For many tasks, automated guided vehicles, robotic arms, or automated small-parts storage systems remain faster and more cost-effective. The potential of humanoid robots lies in activities that have previously been too variable, too granular, or too dispersed for cost-effective automation.
At its core, a humanoid robot is a physical AI system in human form, where the body, senses, and a learning "brain" must work together in real time. Its body typically has several dozen joints, known as degrees of freedom. Each of these requires an actuator consisting of a motor, gearbox, and sensors, which must be compact and powerful, yet compliant enough for safe interaction with people and objects. One of the greatest technical challenges is the hand. To use tools or grasp various parts, it requires many degrees of freedom as well as force and tactile sensors in a very confined space. Bipedal walking requires constant balance control, which is now frequently trained in simulations using reinforcement learning and then transferred to the actual robot. The robot perceives its environment via cameras, depth sensors, and sometimes LiDAR, while determining its own position using inertial sensors and joint encoders. Robotics foundation models, such as vision-language-action models, serve as the "brain"; these run on an edge computer inside the robot and generate movements based on images and instructions. Battery life limits operating time, which is why fast-charging or autonomously swappable batteries are being developed for multi-shift operations.
The most well-known pilot projects are underway in the automotive industry and contract logistics, for example at BMW, Mercedes-Benz, and GXO. At the BMW plant in Leipzig, the batteries last about three to four hours depending on the task and are changed by the robot itself. The robots take over repetitive tasks in structured environments: moving containers, inserting parts, and providing materials. Independent data on cycle times, failure rates, and economic viability are currently scarce; many figures come directly from the manufacturers.
There are still several unresolved challenges between a successful pilot project and the deployment of hundreds of robots in shift operations. The most critical is reliability: in regular operation, it doesn't matter if a robot can perform a task once, but whether it can complete it thousands of times without error. Closely linked to this is generalization, as new parts, processes, or environments often still require additional training today. Hardware is also not yet fully mature. Dexterous, robust, and affordable hands, as well as long-lasting actuators, remain technically demanding. For mobile, bipedal robots that are prone to falling, appropriate safety standards are still being developed. Furthermore, acquisition, integration, and maintenance costs must be competitive compared to alternatives, which is why many providers are turning to rental models (Robots-as-a-Service). Another bottleneck is data. Robotics models require large amounts of realistic training data, and information on how work actually unfolds in real-world facilities is scarce. Learn more on the page Training Data for Physical AI.
For most businesses, humanoid robots are not yet a standard purchase, but the time to start preparing is now. It begins with understanding your own operations: which manual tasks occur, how often, how long do they take, and how physically demanding are they? Only objective process data can reveal where a humanoid robot would actually make sense. Once you have this foundation, you can select suitable tasks. Ideal entry-level tasks are repetitive, take place in structured environments, and are ergonomically taxing for humans, such as container handling or material supply. By measuring times, travel distances, and physical strain today, you will be able to prove whether a robot truly delivers added value later on.
