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Siemens and Humanoid's Physical AI Revolution in Industry: Human-Like Robots in Factories with NVIDIA Technology

Cengiz Özemli

Academic
  • Dokuz Eylül Üniversitesi
  • 1776794552700-siemens-news-april-21-2026-web.png

    Siemens and Humanoid, in collaboration with NVIDIA, have taken a significant step forward by testing human-like robots in fully AI-powered adaptive manufacturing facilities. The HMND 01 Alpha robot, successfully implemented at Siemens' electronics factory in Erlangen, Germany, demonstrated the real-world potential of physical AI by undertaking autonomous logistics tasks.

    ### The Rise of Physical AI in Manufacturing
    Physical AI aims to revolutionize manufacturing processes by enabling machines to perceive, reason, and act in the physical world. This technology requires high-performance AI computing, simulation, advanced robotic platforms, and a robust industrial automation infrastructure.

    The HMND 01 Alpha autonomously performed tasks such as picking up, transporting, and placing containers in Siemens' logistics operations, supporting human operators. The robot met all performance targets with a capacity to transport 60 containers per hour, operating for over 8 hours, and achieving a successful pick-and-place rate of over 90%.

    ### Industrial Integration with Siemens Xcelerator
    The true value of a humanoid robot comes from its ability to work synchronously within a manufacturing facility, exchanging real-time data with other production systems, autonomous vehicles, and human operators. Siemens provides this integration through its Xcelerator portfolio, offering layers such as digital twin, AI-powered perception, control systems, PLC-robot interfaces, and fleet management.

    This comprehensive automation infrastructure allows robots to work harmoniously and efficiently with other systems in the factory. This creates a factory-standard model for the effective use of humanoid robots in production environments.

    ### Gaining Intelligence and Speed with NVIDIA Technologies
    Humanoid integrated NVIDIA's Jetson Thor edge compute platform, Isaac Sim simulation tool, and Isaac Lab reinforcement learning systems into the HMND 01 robot. Through simulation-driven design, actuator selection, joint stiffness, and mass distribution were optimized in a virtual environment, reducing prototype development time from 18-24 months to 7 months.

    As Deepu Talla, Vice President of NVIDIA Robotics and Edge AI, stated, "The factories of the future need robots that can autonomously perceive and adapt alongside humans. The collaboration between Siemens and Humanoid, leveraging NVIDIA's comprehensive physical AI technologies, is making this goal a reality."

    ### Humanoid Company and HMND 01 Features
    UK-based Humanoid developed the HMND 01 Alpha robot specifically for industrial environments. This robot, featuring omnidirectional wheeled movement and advanced manipulation capabilities, is powered by an AI framework called KinetIQ. It can perform complex tasks in collaborative workspaces with humans.

    • Omnidirectional wheeled platform
    • Advanced gripping and manipulation skills
    • AI-based adaptation and learning

    Artem Sokolov, CEO of Humanoid, states that the collaboration with Siemens and NVIDIA combines AI infrastructure and industrial expertise in this field, proving that robots are ready to work in real factories.

    ### About Siemens
    Siemens AG is a global technology leader in industry, infrastructure, mobility, and healthcare. It connects the digital and real worlds to increase factory efficiency, make cities more livable, and make transportation sustainable. Siemens generated €78.9 billion in revenue in fiscal year 2025 and employs approximately 318,000 people.

    ### About Humanoid
    Founded in 2024, UK-based Humanoid is an innovation company focused on developing advanced humanoid robots to augment human capabilities. With over 200 engineers and researchers in offices in London, Boston, and Vancouver, it produces commercially viable robotic solutions.
     
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