Humanoid robots are coming. Which industries are poised for it?

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You may soon start seeing humanoid robots outside of just tech demos and trade show exhibits.

Between advancements in AI and labor shortages, humanoids are being pushed to the forefront of disparate industries looking to augment their workforce.

And similar to other autonomous robots like autonomous guided vehicles (AGVs) or autonomous mobile robots (AMRs), humanoids will be taking certain sectors by storm. High up on that list are manufacturing, construction, healthcare, and warehousing, which are uniquely positioned to digitally transform their operations.

To help understand the lay of this humanoid landscape, we teamed up with the industrial AI and robotics experts at Siemens to learn more about the tech supporting these robots, their potential impact, and the barriers around adoption. Here’s what we found.

AI in physical form

Physical AI is the application of AI beyond the digital realm. The idea is that AI systems can perceive, reason, and act in the physical world in real time. What makes this possible? It’s a combination of:

• advanced sensing technologies (e.g., vision, audio, depth, and motion)
• advanced semiconductors that are increasingly robust
• physical systems such as robots and machines

Industrial AI plays a key role in physical AI as well. This kind of artificial intelligence is purpose-built for real-world industrial environments, where reliability, safety, precision, and scalability are essential. Industrial AI helps companies analyze data, recognize patterns, predict outcomes, and support faster decisions across engineering and operations.

Understanding physical AI itself, however, requires stepping back from its industrial applications to see what distinguishes it from traditional automation. Rather than following rigid pre-programmed instructions, it enables systems to interpret unstructured environments, adapt to new situations, and make autonomous decisions.

In order to be workforce-ready, robots need the ability to be improved and upgraded throughout their life cycle. That’s leading to robots being software-defined products. Humanoids, for example, could start with limited task coverage. But their value comes from the ability to expand those behaviors, skills, and performance over time.

As it evolves, physical AI can acquire new skills, better perception-to-action performance, and broader task coverage. Rather than relying on hardware redesign or software algorithms, physical AI can “learn” to support an ever-expanding set of capabilities.

The digital twin

Before physical AI can be applied to the factory floor, it must first be trained. And that’s where a digital twin comes into play. It’s pretty much what it sounds like, folks. A digital twin is a digital representation of a physical asset or process that can be used to simulate real-world behavior across the lifecycle.

In this case, it provides the foundation needed to bring humanoid robots into the workforce: The digital twin helps engineers and designers train, validate, and refine robots before scaling in the real world.

Using the digital twin, a virtual environment can be created using hundreds of thousands of scenarios and digitally created synthetic training data. The key element here is simulating real workflows. The digital twin can generate synthetic data for edge cases, train and fine-tune policies, validate safety constraints, and stress-test performance across variability.

From there, the AI model can safely learn real-world physics without the need to produce large quantities of expensive, real-world data. Physical AI models can improve faster with less risk and less downtime.

By using true-to-life virtual equipment, there’s also no risk of damaging expensive production equipment. In essence, the digital twin can significantly shorten the time and resources needed to train and validate physical AI models while still producing robust and reliable results.

Forecasting the future

The biggest bottleneck for humanoid adoption? Integrating robotics, controls, safety, data, and operations. That’s why Siemens developed system-level integration that simplifies engineering, commissioning, and operations. It allows deployments to be replicated across OT and IT. Plus, an open interoperable ecosystem helps accelerate progress across the full stack: industrial AI, software-defined platforms, and robotics hardware.

Siemens helps enable interoperability and partner solutions so organizations can build, operate, and continuously improve robotic systems end-to-end. Curious to learn more? Start here.