Why Robotics Teams Use Simulation Testing

A few years ago, I learned the hard way that jumping straight into hardware, sensors, motors, and wiring can lead to costly mistakes and late-night headaches. That’s when I discovered the true importance of #simulation in robotics and engineering. During the early phase of my final-year thesis, I spent weeks recreating our school cafeteria with Iman Tokosi in Blender, exporting it as an SDF model and loading it into Gazebo using #ROS2. Suddenly, I could drive a virtual robot through aisles and around tables without the fear of damaging anything real. It was challenging and eye-opening, and it saved me countless hours and resources. Then came the moment that changed everything: integrating #SLAM so the robot could build its own map while moving, and setting up #Nav2 to let it plan and follow paths autonomously. Watching it navigate the environment with precision and independence was a powerful confirmation that the system worked. Now, imagine a world where every structure, product, and system is simulated down to the smallest detail. The result? Reduced costs, faster development, increased reliability, enhanced safety, and stronger adherence to standards. Some may still view simulation as “just for show,” but I’ve experienced firsthand that it’s the foundation of true innovation. Are you leveraging simulation in your next robotics or engineering project? Let’s connect and exchange ideas!

𝗛𝗨𝗠𝗔𝗡𝗢𝗜𝗗 𝗥𝗢𝗕𝗢𝗧𝗜𝗖𝗦: 𝗪𝗵𝗲𝗿𝗲 𝗦𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗕𝗲𝗴𝗶𝗻𝘀 𝗮𝗻𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗔𝘄𝗮𝗸𝗲𝗻𝘀 Deep Learning in 3D Simulation is not a lab exercise. It is the moment we begin to teach machines how to exist. Not to repeat motions. Not to merely follow code. But to learn, adapt, balance, reason, and act with purpose. In my project we are not just building robots. We are building a new class of intelligence that experiences the world before it ever touches reality. In these simulation environments, gravity does not remain constant. Terrain does not always cooperate. Obstacles change shape. Sensors lie. Friction shifts. And the humanoid must still stand, walk, grasp, adjust, optimize, and choose its next step. Domain randomization, reinforcement learning, hierarchical policies, and graph neural dependencies no longer sound like academic theory. They become survival tools. Machines begin to develop strategies. They learn how to carry payloads across unstable rubble. They learn energy discipline when battery is low and temperature is high. They learn trajectory planning not as geometry, but as survival logic. When you combine photorealistic environments from Isaac Sim, contact-perfect physics in MuJoCo, embodied navigation in Habitat, and emergent behavior in Unity, you begin to see something different. You see machines build experience. You see memory. You see policy retention. You see adaptation. You see the beginning of abstract perception where simulation is not just testing, but education. The difference between teaching a robot how to walk, and letting it discover how to navigate a collapsing environment with intelligence and intent. This is where humanoid robotics becomes human oriented. Robots that can open doors without templates. Carry supplies without pre-programmed routes. Coordinate convoys. Assist in evacuation. Make real time physical decisions aligned with mission objectives, not static instructions. Simulation gives us time compression. We can give a single humanoid what would have taken humans years of trial. We can compress thousands of failures into one informed policy. This is how we transform capability. Not automation. Cognitive autonomy. Not motion planning. Motion intelligence. Not digital twins. Learning twins. We are building humanoids that do not just survive the environment. They learn from it. If you are in advanced simulation, deep learning pipelines, physics engines, reinforcement learning, biomechanics, embodied cognition, ROS2, Isaac Sim, MuJoCo, Omniverse, Habitat, Unity, Unreal, LLM integration, perception or policy optimization… Then we should not be working apart. We should be building this together. And for those ready to build the next generation of thinking humanoids Singularity Systems is now accepting collaborators, researchers, engineers, architects, and visionaries. Let’s teach machines how to exist. #changetheworld #3D #unity

The new Newton physics engine is a game-changer for simulation. It’s not just faster — it’s truer to the real world. ✅ Why it matters: • Real robots operate in messy environments — dust, soil, cables, cloth, fluids. • Traditional simulators simplify or ignore those details. • Newton introduces physics intrinsics — friction, deformation, and material interaction — that make simulations behave like real life. ✅ What this means: • More accurate training data for AI and robotics. • More reliable behavior transfer from sim-to-real. • Fewer surprises when your model meets the real world. ✅ Key advantage: • GPU-native architecture runs thousands of environments at once. • Each environment obeys real-world physical laws. • Simulations now capture how dust swirls, fabric folds, or soil compresses — all in real time. ✅ The result: Simulation that doesn’t just look real — it behaves real. That’s the next frontier for robotics, AI, and digital twins. At FS Studio, we’re already integrating these new physics intrinsics into our synthetic data and digital twin workflows — pushing simulations to mirror the real world more faithfully than ever before. Check out Newton for yourself: https://lnkd.in/gaPVyGin

𝐑𝐨𝐛𝐨𝐭𝐬 𝐚𝐫𝐞 𝐠𝐨𝐢𝐧𝐠 𝐭𝐨 𝐬𝐚𝐯𝐞 𝐔𝐒 𝐌𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠 and that will be accomplished by 𝐥𝐞𝐯𝐞𝐫𝐚𝐠𝐢𝐧𝐠 𝐝𝐢𝐠𝐢𝐭𝐚𝐥 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 and tools to make it easier for companies to sell, plan, and deploy automation solutions at a much lower risk. I talked with Brian Knutson, MBA from Visual Components to learn how about the latest they are brining to help companies with #manufacturing simulation and offline programming! Here are 5 Advantages for Simulation and Offline Programming (OLP): ▶️ 𝐑𝐢𝐬𝐤 𝐑𝐞𝐝𝐮𝐜𝐭𝐢𝐨𝐧: Before deploying a robotic solution in the real-world, OLP allows programmers to create, test, and validate their programs in a virtual environment which reduces collisions and potential errors. ▶️ 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 𝐚𝐧𝐝 𝐓𝐢𝐦𝐞-𝐒𝐚𝐯𝐢𝐧𝐠𝐬: In traditional robotic programming, the robot might need to be taken offline or stopped to allow for new programming or adjustments. OLP can be developed and refined in continuous operation. ▶️ 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: When we can visualize the robot's path and tasks in a virtual environment, engineers can find the most efficient paths, optimize cycle times, and reduce unnecessary movements. ▶️ 𝐄𝐚𝐬𝐞 𝐨𝐟 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠: We can allow new employees when robotic hardware might not be readily available to become familiar with robot operations and processes. ▶️ 𝐅𝐥𝐞𝐱𝐢𝐛𝐢𝐥𝐢𝐭𝐲: Manufacturing is constantly changing. OLP allows companies to try and test different variations with little risk to current or future production. #VisualComponents also just released their new 4.10 update to improve the integration of CAD models from CADENAS USA! If you want to learn more about Visual Components, I'll put some links down below, along with some of the videos I use to highlight #robotics on #LinkedIn. #TheManufacturingMillennial #VisualComponentsPartner #Robotics #Robots #Automation #DigitalSimulation #technology #engineering

For simulation to be viable for robotics, especially for creating AI-generated robot behavior, it is critical that robot behaviors created in simulation can be seamlessly translated to the real world AND the data from the real world can get back to that simulation! This allows for closed-loop learning of robot behaviors in simulation, using information about how the system actually performs. In this latest video, we show how READY Robotics' ForgeOS and NVIDIA Omniverse can provide this closed loop, by enabling robot programming in simulation, seamless transfer to the real world, and providing a path for data to be sent back to simulation. For modern AI algorithms to perform correctly, they need data not just of the robot's movements, but everything that the robot interacts with in its environment. This is why ForgeOS not only sends robot motion back to Omniverse but also the state of all of the tooling, as shown by the tool changer's behavior being accurately represented when mirroring the real system. ForgeOS is also able to surface sensor data, machine state, object locations, and more from the real system back to Omniverse. The ability to exfiltrate the traditionally siloed data in a robotic cell in the factory is something that ForgeOS does out of the box, without any additional IoT devices, and it ties directly back to NVIDIA's Isaac Sim. #ai #ml #manufacturing #robotics #automation #futureofwork