{"id":73996,"date":"2026-06-15T12:03:25","date_gmt":"2026-06-15T16:03:25","guid":{"rendered":"https:\/\/blogs.sw.siemens.com\/simcenter\/?p=73996"},"modified":"2026-06-15T12:03:28","modified_gmt":"2026-06-15T16:03:28","slug":"system-simulation-for-humanoid-robots-turning-complexity-into-competitive-advantage-with-simcenter-amesim","status":"publish","type":"post","link":"https:\/\/blogs.sw.siemens.com\/simcenter\/system-simulation-for-humanoid-robots-turning-complexity-into-competitive-advantage-with-simcenter-amesim\/","title":{"rendered":"System Simulation for humanoid robots &#8211; Turning complexity into competitive advantage with Simcenter Amesim"},"content":{"rendered":"\n<p>Humanoid robots are no longer science fiction. They are quickly becoming a strategic technology for industry, logistics, healthcare, and service applications. Yet designing a humanoid robot remains one of the most complex engineering challenges ever attempted.<\/p>\n\n\n\n<p>What separates successful humanoid programs from stalled prototypes is not just better hardware \u2014 it is the ability to&nbsp;<strong>understand, predict, and optimize the entire system<\/strong>&nbsp;early on.<\/p>\n\n\n\n<p>This is where&nbsp;<strong>system simulation with Simcenter Amesim<\/strong>&nbsp;becomes a decisive enabler.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"470\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Humanoid_Robots_System_Simulation-c-1024x470.png\" alt=\"\" class=\"wp-image-75717\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Humanoid_Robots_System_Simulation-c-1024x470.png 1024w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Humanoid_Robots_System_Simulation-c-600x275.png 600w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Humanoid_Robots_System_Simulation-c-768x353.png 768w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Humanoid_Robots_System_Simulation-c-900x413.png 900w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Humanoid_Robots_System_Simulation-c.png 1183w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Digital Twins for Robotics (humanoids or quadrupeds) with Simcenter System Simulation solutions<\/figcaption><\/figure><\/div>\n\n\n<h2 class=\"wp-block-heading\"><a><strong>Why System Simulation changes everything &#8211; <\/strong><\/a><strong>Designing intelligence, not just machines<\/strong><\/h2>\n\n\n\n<p>Humanoid robots are much more than mechanical assemblies. Every design choice impacts performance, safety, energy consumption, autonomy, and ultimately feasibility. Tight packaging, thermal limitations, power constraints, and real\u2011time control requirements all compete within a human\u2011sized form factor.<\/p>\n\n\n\n<p>System simulation allows teams to answer critical questions&nbsp;<strong>before hardware exists<\/strong>:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Can this robot walk dynamically without sacrificing stability?<\/li>\n\n\n\n<li>How much energy will a real mission truly consume?<\/li>\n\n\n\n<li>What is the impact of actuator choice on autonomy and weight?<\/li>\n\n\n\n<li>How do control strategies behave under realistic physics?<\/li>\n<\/ul>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-medium\"><img loading=\"lazy\" decoding=\"async\" width=\"600\" height=\"313\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Humanoid_Robots_Mechatronics-b-600x313.png\" alt=\"\" class=\"wp-image-75713\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Humanoid_Robots_Mechatronics-b-600x313.png 600w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Humanoid_Robots_Mechatronics-b-768x401.png 768w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Humanoid_Robots_Mechatronics-b-900x470.png 900w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Humanoid_Robots_Mechatronics-b.png 988w\" sizes=\"auto, (max-width: 600px) 100vw, 600px\" \/><figcaption class=\"wp-element-caption\">System Simulation in Humanoids &#8211; What is a mechatronic system?<\/figcaption><\/figure><\/div>\n\n\n<p>With Simcenter Amesim, engineers create a&nbsp;<strong>virtual digital twin<\/strong>&nbsp;that connects mechanics, electronics, controls, and energy into one coherent system. This transforms humanoid development from trial\u2011and\u2011error into&nbsp;<strong>informed design decision\u2011making<\/strong>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><a><strong>Locomotion and mobility &#8211; <\/strong><\/a><strong>The challenge of building something human<\/strong><\/h2>\n\n\n\n<p>Movement defines a humanoid robot. Walking, climbing, balancing, and recovering from disturbances are what allow robots to operate in human environments rather than specially adapted ones.<\/p>\n\n\n\n<p>System simulation enables engineers to:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Explore walking and dynamic motion early<\/li>\n\n\n\n<li>Validate balance and stability strategies virtually<\/li>\n\n\n\n<li>Understand how arms, legs, and torso interact dynamically<\/li>\n\n\n\n<li>Test mobility across slopes, steps, and complex terrain<\/li>\n<\/ul>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-medium\"><img loading=\"lazy\" decoding=\"async\" width=\"600\" height=\"367\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/04\/Humanoid_Robots_Berkeley_CAD_3DMEC-600x367.png\" alt=\"\" class=\"wp-image-73999\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/04\/Humanoid_Robots_Berkeley_CAD_3DMEC-600x367.png 600w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/04\/Humanoid_Robots_Berkeley_CAD_3DMEC-768x469.png 768w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/04\/Humanoid_Robots_Berkeley_CAD_3DMEC.png 872w\" sizes=\"auto, (max-width: 600px) 100vw, 600px\" \/><figcaption class=\"wp-element-caption\">The CAD file is directly imported into Simcenter Amesim to get the 3D mechanics (multibody)<\/figcaption><\/figure><\/div>\n\n\n<p>Instead of discovering limitations late in physical testing, teams can refine locomotion concepts digitally \u2014 faster, safer, and with far less cost.<\/p>\n\n\n\n<figure class=\"wp-block-video aligncenter\"><video autoplay controls loop muted src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/04\/Simcenter_Amesim_Humanoid_Robot_Berkeley_Backflip.mp4\"><\/video><figcaption class=\"wp-element-caption\">Balance and stability using junctions and bodies from the 3D Mechanical library<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><a><strong>Actuation systems &#8211; <\/strong><\/a><strong>Teaching machines to move like us<\/strong><\/h2>\n\n\n\n<p>Actuators are the muscles of the humanoid robot \u2014 and the source of many design trade\u2011offs. Power density, efficiency, responsiveness, weight, and thermal behavior all matter.<\/p>\n\n\n\n<p>Simcenter Amesim supports a wide range of actuation technologies, including electric, hydraulic, and hybrid architectures. This allows teams to:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Compare architectures objectively<\/li>\n\n\n\n<li>Optimize torque, speed, and efficiency<\/li>\n\n\n\n<li>Balance performance against mass and energy usage<\/li>\n\n\n\n<li>Reduce over\u2011design and unnecessary margins<\/li>\n<\/ul>\n\n\n\n<p>By simulating actuation at system level, humanoid developers can&nbsp;<strong>select the right technology with confidence<\/strong>, rather than relying on assumptions.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-medium\"><img loading=\"lazy\" decoding=\"async\" width=\"600\" height=\"193\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Hand_Orthosis_Exoskeleton_with_Ball-a-600x193.png\" alt=\"\" class=\"wp-image-75707\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Hand_Orthosis_Exoskeleton_with_Ball-a-600x193.png 600w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Hand_Orthosis_Exoskeleton_with_Ball-a-768x247.png 768w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Hand_Orthosis_Exoskeleton_with_Ball-a-900x290.png 900w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Hand_Orthosis_Exoskeleton_with_Ball-a.png 953w\" sizes=\"auto, (max-width: 600px) 100vw, 600px\" \/><figcaption class=\"wp-element-caption\">Compact and lightweight hand exoskeletons<\/figcaption><\/figure><\/div>\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-medium\"><img loading=\"lazy\" decoding=\"async\" width=\"600\" height=\"309\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Hnad_Five_Fingers_and_Wrist-a-600x309.png\" alt=\"\" class=\"wp-image-75708\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Hnad_Five_Fingers_and_Wrist-a-600x309.png 600w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Hnad_Five_Fingers_and_Wrist-a-1024x528.png 1024w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Hnad_Five_Fingers_and_Wrist-a-768x396.png 768w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Hnad_Five_Fingers_and_Wrist-a-900x464.png 900w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Hnad_Five_Fingers_and_Wrist-a.png 1218w\" sizes=\"auto, (max-width: 600px) 100vw, 600px\" \/><figcaption class=\"wp-element-caption\">Modeling of the hand with its five fingers and its wrist<\/figcaption><\/figure><\/div>\n\n\n<h2 class=\"wp-block-heading\"><a><strong>Manipulation and dexterity &#8211; <\/strong><\/a><strong>From motion to meaning<\/strong><\/h2>\n\n\n\n<p>True usefulness comes from interaction. Humanoid robots must grasp, manipulate, and <a href=\"https:\/\/blogs.sw.siemens.com\/tecnomatix\/the-future-of-bridging-humans-robots-and-humanoids-with-process-simulate-software-video\/\">adapt to a wide variety of objects<\/a> \u2014 often with human\u2011like dexterity.<\/p>\n\n\n\n<p>System simulation makes it possible to:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Virtually prototype hands, grippers, and end effectors<\/li>\n\n\n\n<li>Evaluate grasp stability and contact forces<\/li>\n\n\n\n<li>Explore lightweight and underactuated designs<\/li>\n\n\n\n<li>Optimize mechanisms before building hardware<\/li>\n<\/ul>\n\n\n\n<p>This approach accelerates innovation in dexterous manipulation while reducing costly physical iterations.<\/p>\n\n\n\n<figure class=\"wp-block-video aligncenter\"><video autoplay controls loop muted src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/04\/Simcenter_Amesim_Humanoid_Robot_NAO_Grasping_balls_Walking.mp4\"><\/video><figcaption class=\"wp-element-caption\">NAO Humanoid robot grasping balls and walking in Simcenter Amesim<\/figcaption><\/figure>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-medium\"><img loading=\"lazy\" decoding=\"async\" width=\"600\" height=\"193\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Gripper_materials_handling-a-600x193.png\" alt=\"\" class=\"wp-image-75709\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Gripper_materials_handling-a-600x193.png 600w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Gripper_materials_handling-a-768x247.png 768w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Gripper_materials_handling-a-900x290.png 900w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Gripper_materials_handling-a.png 953w\" sizes=\"auto, (max-width: 600px) 100vw, 600px\" \/><figcaption class=\"wp-element-caption\">Grippers and material handling &#8211; The gripper adapts its profile to handle the object<\/figcaption><\/figure><\/div>\n\n\n<p>The digital twin is now ready for its mechanical parts. Let\u2019s go to other key aspects like power and energy management to ensure the Humanoid Robots stay alive during their complete missions. It\u2019s more at the electric and thermal sides of the components considering their system integration and interactions.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><a><strong>Power and energy systems &#8211; <\/strong><\/a><strong>The invisible constraint<\/strong><\/h2>\n\n\n\n<p>In humanoid robotics, autonomy is everything. Battery size, charging time, thermal limits, and power management directly constrain what a robot can do in the real world.<\/p>\n\n\n\n<p>Simcenter Amesim enables:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Mission\u2011based energy analysis<\/li>\n\n\n\n<li>Battery sizing and range estimation<\/li>\n\n\n\n<li>Evaluation of auxiliary power consumption<\/li>\n\n\n\n<li>Thermal safety and component lifetime assessment<\/li>\n<\/ul>\n\n\n\n<p>Instead of reacting to \u201cbattery anxiety\u201d late in development, teams can&nbsp;<strong>design autonomy as a first\u2011class requirement<\/strong>&nbsp;from day one.<\/p>\n\n\n\n<figure class=\"wp-block-video aligncenter\"><video autoplay controls loop muted src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/04\/Simcenter_Amesim_TwinsWheel_Power_Consumption.mp4\"><\/video><figcaption class=\"wp-element-caption\">Power consumption [W] in the different subsystems depending on mission and speed [m\/s]<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><a><strong>Autonomy and 3D environment &#8211; <\/strong><\/a><strong>Autonomy in a human world<\/strong><\/h2>\n\n\n\n<p>Humanoid robots must operate safely and robustly in environments designed for humans \u2014 not robots. This demands behavior that adapts to the unexpected.<\/p>\n\n\n\n<p>System simulation supports:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Virtual 3D environments and terrain<\/li>\n\n\n\n<li>Sensor integration and perception workflows<\/li>\n\n\n\n<li>Evaluation of navigation and obstacle avoidance<\/li>\n\n\n\n<li>Early validation of control strategies in realistic scenarios<\/li>\n<\/ul>\n\n\n\n<p><a href=\"https:\/\/blogs.sw.siemens.com\/simcenter\/when-medical-disinfection-robots-succeed-its-more-autonomy-and-more-intelligence\/\">By testing autonomy in the virtual world<\/a>, teams dramatically reduce real\u2011world risk while speeding up deployment readiness.<\/p>\n\n\n\n<figure class=\"wp-block-video aligncenter\"><video autoplay controls loop muted src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/04\/Simcenter_Amesim_Disinfection_Robot_in_Unknown_3D_Environment.mp4\"><\/video><figcaption class=\"wp-element-caption\">3D views from the different camera orientations &#8211; Sensor fusion for video processing<\/figcaption><\/figure>\n\n\n\n<p>Well done! The multiphysics aspects of these humanoid mechatronic systems are now being managed effectively. The next challenge is now to develop an optimal controller quickly. Let\u2019s explore the appropriate workflow that combines both online and offline approaches. This includes the use of Artificial Intelligence (AI) techniques such as Reinforcement Learning (RL), or to put it simply: \u201cPhysical AI\u201d, or \u201cPhysics-Induced Machine Learning\u201d, up to the \u201cHybrid Analytics\u201d for the \u201cSim-to-Real\u201d training of the robots.<\/p>\n\n\n\n<p>Let\u2019s have a deeper look at the \u201cSim-to-Real\u201d transfer. It\u2019s the process of training a policy in a physics simulation tool and deploying it on physical hardware, bridging the reality gap with domain randomization.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><a><strong>AI needs physics \u2013 <\/strong><\/a><strong>Not the other way around<\/strong><\/h2>\n\n\n\n<p>Modern humanoids increasingly rely on intelligent control and machine learning to achieve robust behavior. However, collecting real\u2011world training data is slow, expensive, and risky.<\/p>\n\n\n\n<p>Digital twins enable:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Generation of high\u2011quality synthetic training data<\/li>\n\n\n\n<li>Safe exploration of edge cases<\/li>\n\n\n\n<li>Faster training and iteration of AI models<\/li>\n\n\n\n<li>Improved sim\u2011to\u2011real transfer<\/li>\n<\/ul>\n\n\n\n<p><a href=\"https:\/\/blogs.sw.siemens.com\/simcenter\/the-future-of-robot-training\/\">By combining physics\u2011based simulation with AI<\/a>, Simcenter Amesim helps bridge the gap between&nbsp;<strong>theory, training, and real\u2011world performance<\/strong>.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-medium\"><img loading=\"lazy\" decoding=\"async\" width=\"600\" height=\"277\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Quadrupeds_Synthetic_data_Online-a-600x277.png\" alt=\"\" class=\"wp-image-75710\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Quadrupeds_Synthetic_data_Online-a-600x277.png 600w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Quadrupeds_Synthetic_data_Online-a-1024x474.png 1024w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Quadrupeds_Synthetic_data_Online-a-768x355.png 768w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Quadrupeds_Synthetic_data_Online-a-900x416.png 900w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Quadrupeds_Synthetic_data_Online-a.png 1237w\" sizes=\"auto, (max-width: 600px) 100vw, 600px\" \/><figcaption class=\"wp-element-caption\">Data generation in operations is a challenge for machine learning as it usually entails large costs<\/figcaption><\/figure><\/div>\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-medium\"><img loading=\"lazy\" decoding=\"async\" width=\"600\" height=\"269\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Quadrupeds_Synthetic_data_Offline-b-600x269.png\" alt=\"\" class=\"wp-image-75711\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Quadrupeds_Synthetic_data_Offline-b-600x269.png 600w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Quadrupeds_Synthetic_data_Offline-b-1024x459.png 1024w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Quadrupeds_Synthetic_data_Offline-b-768x344.png 768w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Quadrupeds_Synthetic_data_Offline-b-900x403.png 900w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Quadrupeds_Synthetic_data_Offline-b.png 1274w\" sizes=\"auto, (max-width: 600px) 100vw, 600px\" \/><figcaption class=\"wp-element-caption\">Benefits using digital twins to generate synthetic data with high fidelity simulation<\/figcaption><\/figure><\/div>\n\n\n<p>This is about not reinventing the wheel. The robot is already programmed with everything it needs to know about physics. It\u2019s about how the robot adapts to environmental uncertainties \u2014 varying road slopes, uneven terrain, and the different friction characteristics of materials and surfaces.<\/p>\n\n\n\n<p>So, in fact, the robot needs just 10 seconds of real-world data to learn and optimize its behavior, a significant breakthrough in efficient robot training.<\/p>\n\n\n\n<figure class=\"wp-block-video aligncenter\"><video autoplay controls loop muted src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/04\/Simcenter_How-to-train-a-robot_Sim2Real_Legged_Robot_Control.mp4\"><\/video><figcaption class=\"wp-element-caption\">Hybrid analytics combining data and physics using machine learning techniques<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><a><strong>The business impact &#8211; Key benefits for user<\/strong><\/a><strong>s<\/strong><\/h2>\n\n\n\n<p>System simulation is not just an engineering tool \u2014 it is a&nbsp;<strong>strategic accelerator<\/strong>:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Faster time to market<\/li>\n\n\n\n<li>Reduced development risk<\/li>\n\n\n\n<li>Optimized performance and energy efficiency<\/li>\n\n\n\n<li>Higher confidence in design decisions<\/li>\n\n\n\n<li>Scalable innovation from concept to deployment<\/li>\n<\/ul>\n\n\n\n<p>For organizations investing in humanoid robotics, Simcenter Amesim provides a foundation to&nbsp;<strong>build smarter, lighter, more capable robots \u2014 faster than competitors<\/strong>.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-medium\"><img loading=\"lazy\" decoding=\"async\" width=\"600\" height=\"244\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Humanoid_Robots_Takeaways-a-600x244.png\" alt=\"\" class=\"wp-image-75712\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Humanoid_Robots_Takeaways-a-600x244.png 600w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Humanoid_Robots_Takeaways-a-768x312.png 768w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Humanoid_Robots_Takeaways-a-900x365.png 900w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/06\/Humanoid_Robots_Takeaways-a.png 963w\" sizes=\"auto, (max-width: 600px) 100vw, 600px\" \/><figcaption class=\"wp-element-caption\">Powerful, compact and lightweight humanoid robots using Simcenter Amesim<\/figcaption><\/figure><\/div>\n\n\n<h2 class=\"wp-block-heading\"><a><strong>Takeaways &#8211; <\/strong><\/a><strong>The future of humanoid robotics<\/strong><\/h2>\n\n\n\n<p>Let\u2019s summarize the key benefits of using System Simulation to address your challenges when designing Humanoid Robots:<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-medium\"><img loading=\"lazy\" decoding=\"async\" width=\"600\" height=\"336\" src=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/04\/Humanoid_Robots_So_Many_Benefits-600x336.png\" alt=\"\" class=\"wp-image-74025\" srcset=\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/04\/Humanoid_Robots_So_Many_Benefits-600x336.png 600w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/04\/Humanoid_Robots_So_Many_Benefits-768x430.png 768w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/04\/Humanoid_Robots_So_Many_Benefits-395x222.png 395w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/04\/Humanoid_Robots_So_Many_Benefits-900x503.png 900w, https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/04\/Humanoid_Robots_So_Many_Benefits.png 1012w\" sizes=\"auto, (max-width: 600px) 100vw, 600px\" \/><figcaption class=\"wp-element-caption\">Simulation extends outside \u201cDesign\u201d or \u201cManufacture\u201d areas to now reach the \u201cService\u201d area<\/figcaption><\/figure><\/div>\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Humanoid robots are stepping <\/strong>i<strong>nto the real world :<\/strong> Humanoids are transitioning from research labs to real industrial and societal roles \u2014 but success depends on mastering complexity.<\/li>\n\n\n\n<li><strong>Everything affects everything : <\/strong>In humanoids, mechanics, energy, control, and behavior are inseparable.<\/li>\n\n\n\n<li><strong>System simulation changes the game : <\/strong>System simulation shifts humanoid development from trial\u2011and\u2011error to informed decision\u2011making.<\/li>\n\n\n\n<li><strong>From motion to interaction : <\/strong>Dexterity determines whether a humanoid is useful or just impressive.<\/li>\n\n\n\n<li><strong>Autonomy is designed, not discovered : <\/strong>&nbsp;Energy defines availability \u2014 simulation makes autonomy predictable.<\/li>\n\n\n\n<li><strong>Smarter AI starts with better physics : <\/strong>&nbsp;Digital twins generate safe, scalable synthetic data for AI and controls.<\/li>\n<\/ul>\n\n\n\n<p>And finally, a brief outcome:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>The humanoid advantage goes to those who master complexity : <\/strong>The winners in humanoid robotics will be system thinkers.<\/li>\n<\/ul>\n\n\n\n<p>Simcenter Amesim drives innovation for the future of Humanoid Robots. It enables smarter, lighter, more capable humanoids \u2014 starting in the virtual world.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Learn more about Siemens Simcenter Amesim<\/strong><\/h2>\n\n\n\n<p><a href=\"https:\/\/plm.sw.siemens.com\/en-US\/simcenter\/systems-simulation\/amesim\/\" target=\"_blank\" rel=\"noreferrer noopener\">Simcenter Amesim<\/a>&nbsp;is the leading integrated, scalable system simulation platform, allowing system simulation engineers to virtually assess and optimize the performance of mechatronic systems.<\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This blog introduces System Simulation for designing Humanoid Robots virtually with digital twins in Simcenter Amesim. It&#8217;s mechatronics with multiphysics to virtually size and assess the performances of Humanoid Robots with simulations executed in few seconds.<\/p>\n","protected":false},"author":27840,"featured_media":74035,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"spanish_translation":"","french_translation":"","german_translation":"","italian_translation":"","polish_translation":"","japanese_translation":"","chinese_translation":"","footnotes":""},"categories":[1],"tags":[64074,64073,1823,8847,64067,16],"industry":[63664,146,145,156,155],"product":[590],"coauthors":[10813],"class_list":["post-73996","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","tag-humanoids","tag-robotics","tag-simcenter","tag-simcenter-amesim","tag-simcenter-physics-ai","tag-system-simulation","industry-battery","industry-consumer-industrial-electronics","industry-electronics-semiconductors","industry-industrial-machinery","industry-industrial-machinery-heavy-equipment","product-simcenter-amesim"],"featured_image_url":"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2026\/04\/Humanoid_Robots_Cover_v3.png","_links":{"self":[{"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/posts\/73996","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/users\/27840"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/comments?post=73996"}],"version-history":[{"count":5,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/posts\/73996\/revisions"}],"predecessor-version":[{"id":75718,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/posts\/73996\/revisions\/75718"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/media\/74035"}],"wp:attachment":[{"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/media?parent=73996"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/categories?post=73996"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/tags?post=73996"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/industry?post=73996"},{"taxonomy":"product","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/product?post=73996"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/coauthors?post=73996"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}