The robotics industry is undergoing a visible shift in focus. While the public imagination has long been captured by the idea of a fully autonomous, human-shaped machine working alongside people, the actual commercial reality is more nuanced. According to reporting from *The Robot Report*, the current state of the field shows a split: some humanoid robots are indeed in commercial trials, but it is the semi-humanoid and nonhumanoid mobile manipulators that are already arriving on factory floors and in warehouse aisles. These are not speculative concepts; they are operational systems being put to work today.
Mobile manipulators—robots that combine a mobile base with one or more articulated arms—are being positioned as the practical bridge between stationary industrial automation and the more ambitious vision of general-purpose humanoids. The distinction matters. A humanoid form factor is not necessarily a prerequisite for useful work. In many logistics and manufacturing environments, a wheeled platform with a manipulator arm can perform tasks such as picking, placing, and transporting items without the complexity and cost associated with bipedal locomotion.
The industry conversation has also expanded to include the concept of “physical AI.” This term refers to artificial intelligence that is embodied in machines that interact with the physical world, as opposed to purely digital AI that operates in software or data environments. The idea is that robots equipped with advanced AI can adapt to unstructured environments, handle variability, and perform tasks that traditional fixed automation cannot. This is not merely a marketing phrase; it is becoming a framework for how the next generation of robotics is being designed and deployed.
*The Robot Report* has compiled a list of more than 200 verified humanoid robot providers globally, tracking where they are located, what types of systems they are developing, and whether their products are still in development or available for purchase. This level of cataloging suggests a maturing ecosystem, one where investors, manufacturers, and service providers are trying to separate genuine capability from hype. The report also includes a discussion with the CEO of Brightpick, a company that provides mobile manipulators, offering insight into how these systems are being positioned for commercial use.
In parallel, the Pittsburgh Robotics Network has issued an outlook for 2026. Jenn Apicella, the network’s executive director, has examined the trends that are likely to shape the next phase of the industry. The predictions include the rise of affordable humanoids, scaled deployment of autonomous transportation, and the mainstreaming of physical AI. The Pittsburgh region, anchored by decades of research at Carnegie Mellon University, hosts a dense network of more than 250 robotics, AI, and deep tech companies. This ecosystem has become a proving ground for autonomous systems across manufacturing, logistics, transportation, defense, and other sectors. The network’s ecosystem map tracks companies involved in autonomous vehicles, industrial robotics, aerial systems, defense tech, sensors, and AI software.
On the healthcare front, Diligent Robotics has announced plans for Moxi 2.0, the latest generation of its mobile manipulation platform. Moxi is already operating in more than 25 hospitals across the United States, assisting nurses and pharmacy staff with routine tasks such as delivering medications and lab samples. The company describes Moxi 2.0’s AI as representing one of the largest datasets of human-robot interaction. The platform is powered by NVIDIA hardware, and Diligent has expressed enthusiasm about the new NVIDIA IGX Thor platform, which they say pushes the boundaries of AI performance at the edge. This is notable because it shows a concrete, deployed fleet of mobile manipulators in a service environment, not just a pilot project.
Why it matters for European robot service
For European operators of service fleets, these developments carry significant implications. The conversation around mobile manipulators and humanoids is not just about what is technologically possible; it is about how fleets will be constructed, maintained, and serviced in the coming years. The convergence of these two categories—mobile manipulators and humanoids—suggests that the service ecosystem will need to adapt to a broader range of robot form factors and capabilities.
One of the key takeaways from the source material is that mobile manipulators are already being deployed in factories and warehouses. This is not a future scenario; it is happening now. For European logistics and manufacturing companies, this means that the technology is available and proven in at least some commercial settings. The question is not whether to consider mobile manipulators, but how to integrate them into existing operations and service models.
The healthcare example is particularly instructive. Diligent Robotics’ Moxi is operating in over 25 hospitals in the U.S., performing routine delivery tasks. This is a service environment that shares many characteristics with European healthcare facilities: busy corridors, shared spaces, human staff with competing priorities, and a need for reliable, safe automation. If mobile manipulators can succeed in U.S. hospitals, the same use cases are likely relevant for European hospitals, clinics, and care facilities. The service implications are substantial. These robots need maintenance, software updates, battery management, and occasional repairs. Fleet operators will need service partners who understand not just the hardware, but the AI and software stack that makes these systems functional.
The Pittsburgh Robotics Network’s prediction of affordable humanoids is also relevant for Europe. If humanoid robots become more affordable, they will likely enter service fleets in a variety of roles. However, the source material does not specify a timeline or price point for this affordability. What is known is that the trend is being tracked and predicted by industry observers. European service providers should monitor this development, but they should also be cautious about over-committing to a technology that is still in commercial trials for humanoids, as noted in the source material.
The concept of physical AI has direct implications for how service fleets are built and maintained. If robots are equipped with AI that allows them to adapt to new situations, then the service model may shift from one of scheduled, preventive maintenance to one that is more predictive and data-driven. However, the source material does not provide specific details on how this will change service contracts, response times, or spare-part logistics. What can be said is that the trend toward embodied AI is likely to increase the importance of software expertise in robot service. A technician who can only replace a motor or a sensor may not be sufficient; the industry will need people who understand AI models, sensor fusion, and edge computing.
The mention of hyperscale data centers in the source material is another point of relevance. Robots are seen as potential assistants in building and maintaining these facilities. Europe is home to a growing number of data centers, and the demand for automated solutions in this sector is likely to increase. If mobile manipulators and humanoids are deployed in data center construction and maintenance, the service ecosystem will need to support them in environments that are often remote, secure, and highly controlled.
For European robot service providers, the convergence of mobile manipulators and humanoids means that the range of systems they may be asked to service is expanding. The source material indicates that there are more than 200 verified humanoid robot providers globally. Not all of these will succeed, and not all of them will enter the European market. But the sheer number suggests that the landscape is fragmented and evolving. Service providers will need to decide which platforms to support, which partnerships to form, and which skills to develop.
What buyers and operators should know
For buyers and operators considering mobile manipulators or humanoids, the source material offers several points of guidance, though it also leaves many questions unanswered. It is important to distinguish between what is known and what is not disclosed.
First, the known facts: Mobile manipulators are already in use in factories and warehouses. This is stated in the source material and represents a level of commercial maturity. Humanoids, by contrast, are in commercial trials, which implies a less mature state. Buyers should be aware that the two categories are at different stages of readiness. A mobile manipulator may be a safer purchase today than a humanoid, simply because it has more proven deployments.
Second, the healthcare example of Moxi is a concrete data point. The robot operates in over 25 hospitals in the U.S., performing tasks such as delivering medications and lab samples. This is a real, deployed fleet, not a concept. The fact that Diligent Robotics describes Moxi 2.0’s AI as representing one of the largest datasets of human-robot interaction suggests that the company has accumulated substantial operational data. For buyers in healthcare or similar service environments, this is a relevant reference case. However, the source material does not disclose specific performance metrics, uptime figures, or cost data. Buyers should ask vendors for such information directly.
Third, the Pittsburgh Robotics Network’s outlook predicts affordable humanoids and scaled autonomous transportation as part of the next wave of physical AI. This is a prediction, not a guarantee. The source material does not provide a timeline or specific cost targets. Buyers should treat this as a directional signal, not a procurement guideline. It is reasonable to expect that humanoid prices will decline over time, but the pace and magnitude of that decline are not specified.
Fourth, the role of NVIDIA hardware is mentioned in the context of Moxi. The platform is NVIDIA-powered, and Diligent has commented on the NVIDIA IGX Thor platform’s potential for AI performance at the edge. For buyers, this suggests that the choice of compute platform is a significant factor in mobile manipulator performance. Edge AI capability—processing data on the robot itself rather than in the cloud—is likely to be important for latency, reliability, and privacy. However, the source material does not provide benchmarks or comparisons between different compute platforms.
Fifth, the source material mentions that AI is both creating and serving the demand for hyperscale data centers. Robots are seen as potential assistants in building and maintaining these facilities. For buyers in the data center sector, this is a potential use case to explore. However, the source material does not provide examples of actual deployments in data centers. It is an emerging opportunity, not an established one.
Buyers should also be aware of what is not disclosed. The source material does not provide specific information on service requirements, maintenance intervals, spare-part availability, or total cost of ownership for mobile manipulators or humanoids. It does not mention SLA numbers, response times, or spare-part lead times. These are critical factors for any fleet operator, and they must be obtained from vendors through direct inquiry and contractual negotiation. The absence of this information in the source material is not an oversight; it simply reflects the fact that the reports focus on technology trends and market predictions, not on service-level details.
Operators should also consider the skills required to maintain these systems. Mobile manipulators combine mobility, manipulation, sensing, and AI. Servicing them requires a multidisciplinary skill set that may not be present in a traditional industrial robot service team. The source material does not address training or certification requirements, but it is reasonable to infer that the complexity of these systems will demand new competencies.
Finally, the source material emphasizes the importance of physical AI as a concept that is enabling the future of automation. For buyers, this means that the software and AI capabilities of a robot are as important as its mechanical specifications. A robot with a strong AI stack may be more adaptable and easier to integrate than one with superior hardware but weaker intelligence. Buyers should evaluate the AI capabilities of any system they are considering, including how it handles edge cases, how it learns from new data, and how it interacts with human workers.
In summary, the convergence of mobile manipulators and humanoids is a real trend with practical implications for service fleets. Mobile manipulators are already deployed in factories, warehouses, and hospitals. Humanoids are in trials and are expected to become more affordable. Physical AI is the enabling technology behind both. For European buyers and operators, the message is to stay informed, ask detailed questions, and be prepared for a service ecosystem that will need to handle a wider variety of robot form factors and capabilities than ever before.
Sources
Published by Vigla Media OÜ (Estonia).