By: Michael Munsey, Vice President of Semiconductors for Siemens Digital Industries Software
A new generation of humanoid robots is moving from science fiction toward the factory floor, driven by advances in semiconductors and artificial intelligence (AI). But for original equipment manufacturers (OEMs), the opportunity is not simply to build machines that can see, feel and perceive; it is to engineer complex, software-defined systems that can act reliably and improve over time in real-world environments.
Their effectiveness as collaborative tools in the workplace will depend on more than physical dexterity. Humanoids need powerful semiconductor-based compute platforms capable of processing complex, real-time workloads, coordinating software and hardware decisions and supporting safe behavior in dynamic settings. The successful integration of humanoids will depend on powerful computing architectures that enable robots to adapt to changing environments and perform a wide variety of tasks while maintaining safe working conditions.
The pinnacle of software-defined systems
For manufacturers, this marks an important shift. Humanoids are not simply another class of robot; they are a convergence point of several domains, from software-defined products and AI to advanced semiconductor design and connected automation. Managing that complexity requires a digital thread that connects decisions across disciplines, from the chip architecture to the finished machine and eventually to the deployed fleet.
Much of the attention around humanoids focuses on their form factor. Their human‑centric design matters because many environments were built around a human’s scale, reach and movement. As a result, organizations can introduce humanoids into existing workplaces with minimal disruption.
Form factor, however, is only half the picture. Humanoid intelligence and adaptability, enabled entirely through AI, separates them from other automated guided vehicles or even autonomous mobile robots. Humanoids are fundamentally software‑defined products, made possible through the convergence of hardware and software advancements. Additionally, improvements in battery efficiency, semiconductor performance and AI capabilities have reached a level where humanoid systems are now practical and scalable.
Recent advancements in vision sensors, perception models and control systems have helped machines recognize more of the world around them. Physical AI takes this further by connecting perception, sensor feedback and motion planning so robots can interact with real-world environments. For humanoids, intelligence is not limited to recognizing an object; it must extend to deciding how to respond safely in context. Powered by physical AI, humanoids could eventually operate in legacy environments and successfully complete several different types of tasks. But that flexibility will depend on continued training and validation. Rather than being programmed once for a fixed task, future humanoids will learn from simulated and real-world experience, with each update tested against safety and reliability requirements before it is deployed more broadly.
Similar to human workers, humanoids must be prepared for the environments where they will operate. The challenge is that most human-centric settings are rarely static. This is where AI comes in to help humanoids accommodate new scenarios through generalization from prior experience. Using AI to train humanoids, businesses can ensure their robots can handle varied tasks and continuously learn in unpredictable environments. This level of adaptability is only possible through semiconductors. Semiconductors provide the computing foundation that can process multiple streams of sensory data while coordinating motor feedback in real time. Physical AI adds another layer of computational complexity to humanoids. The compute architecture must process not only reasoning and decision-making but also constant streams of sensory data and motor-control feedback, which requires highly integrated hardware and software architectures that can coordinate multiple systems simultaneously and in real time.
Engineering the humanoid nervous system
On top of enabling complex compute performance, humanoid semiconductors must operate reliably in demanding environments. Factory floors, data centers and locations with high electrical interference can introduce heat and interference, which can affect how the humanoids operate. As more software and AI workloads run on processors, cooling becomes an increasingly important design constraint and remains the primary limitation of semiconductor performance. Some businesses have already deployed unique packaging techniques with promising outcomes that can distribute workloads more efficiently across various types of silicon to reduce heat. For example, heterogeneous integrated circuits (ICs) combine multiple chips in a single package and stack them. These chips separate out tasks by their compute intensity and run less intense processes on pieces of silicon that process slower; thereby, dropping the temperature of those parts, which results in an overall temperature profile that’s lower on the entire package. Efficient semiconductor operation is about more than guaranteeing efficient operations. It is also critical to ensure the safety of human workers. Humanoids need enough computing power to make split-second decisions, such as the ability to switch instantly from performing a task to avoiding a collision or protecting a nearby person. This requires software and processing architecture that must support rapid decision-making to assist these rapid priority changes without hesitation.
This is where the digital thread becomes essential. Each decision about software behavior, mechanical design, safety logic and manufacturing readiness affects semiconductor architecture. Connecting those decisions across the lifecycle helps engineering teams manage complexity and move from concept to deployment with greater confidence.
Engineers must account for environmental conditions early in the semiconductor design process to improve reliability and circumvent performance issues. A comprehensive Digital Twin allows engineers to model and validate the entire humanoid system before physical hardware is built. Engineers can perform trade studies, allocate requirements and evaluate software/hardware decisions early in development. Teams can continuously test software against virtual hardware, evaluate the chip’s thermal performance, analyze power consumption and verify safety requirements throughout development. Additionally, the Digital Twin can support the robot learning process after deployment. As humanoids receive software updates and AI-driven improvements, engineers can validate those changes virtually before introducing them into physical operations. Over time, validated improvements could be shared across a fleet, creating a feedback loop between simulation and real-world performance. That loop is what makes software-defined systems powerful: they can improve over time while maintaining the governance needed for widescale use.
The semiconductor foundation for scalable humanoids
Semiconductors are the foundation that make humanoids practical at an industrial scale. At their core, humanoids are software-defined systems whose capabilities will largely come from the integration of software, electronics and mechanics rather than mechanical hardware alone. Without continued advances in semiconductor technology, humanoid robots would not be feasible. With the right digital engineering approach, however, they can move from pilot to production. Through advancements in semiconductors and physical AI, combined with AI-powered upgrades and the comprehensive Digital Twin, we at Siemens envision humanoids that can become progressively smarter, safer and more capable over time. The companies that succeed will be those that treat humanoids not as standalone machines, but as complex industrial systems.
For more about semiconductors and the humanoid nervous system, check out The Industry Forward Podcast: Inside humanoid robotics: software and semiconductors – Siemens Software Podcast Network.
To learn how Siemens utilizes the Digital Twin to help our partners design and deploy their next fleet of humanoid robots, click here.
About the author:
Michael Munsey is the Vice President of Semiconductors forSiemens Digital Industries Software. In this role, Munsey is responsible for setting the strategic direction for the company with a focus on helping customers drive unprecedented growth and innovation in the semiconductor and electronics industries through Digital Transformation.
Before joining Siemens in 2021, Munsey spent his career working in positions of increasing responsibility across the semiconductor and electronics industries where he did everything from leading cross-functional teams to drive product creation and executing business development in new regions to setting the vision for corporate strategy. He began his career as a designer at IBM more than 35 years ago and has the distinction of contributing to products that are currently in use on two planets: Earth and Mars, the later courtesy of his work on the Mars Rover.
Munsey holds a BSEE in Electrical and Electronics Engineering from Tufts University.
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