Simulation Methods for Robotics Teams

Build your first robot in simulation! 👾 📌 If you’re self-learning robotics, this is genuinely one of the better repos to save for later. NVIDIA Robotics released a "Getting Started with Isaac Sim" tutorial series covering everything from building your first robot to hardware-in-the-loop deployment. What's inside? → Building Your First Robot Explore the Isaac Sim interface, construct a simple robot model (chassis, wheels, joints), configure physics properties, implement control mechanisms using OmniGraph and ROS 2, integrate sensors (RGB cameras, 2D lidar), and stream sensor data to ROS 2 for real-time visualization in RViz. → Ingesting Robot Assets Import URDF files, prepare simulation environments, add sensors to existing robot models, and access pre-built robots to accelerate development. → Synthetic Data Generation Learn perception models for dynamic robotic tasks, understand synthetic data generation, apply domain randomization with Replicator, generate synthetic datasets, and fine-tune AI perception models with validation. → Software-in-the-Loop (SIL) Build intelligent robots, implement SIL workflows, use OmniGraph for robot control, master Isaac Sim Python scripting, deploy image segmentation with ROS 2 and Isaac ROS, and test with and without simulation. → Hardware-in-the-Loop (HIL) Understand HIL fundamentals, learn NVIDIA Jetson platform, set up the Jetson environment, and deploy Isaac ROS on Jetson hardware. The progression makes sense: start with basics (build a robot), add perception (sensors and data), generate training data (synthetic generation), develop software (SIL), then deploy to hardware (HIL). Each module builds on the previous one. For robotics teams, this is the path to faster iteration. Simulate first, validate in software-in-the-loop, generate synthetic training data at scale, then deploy to hardware with confidence. 🎓 If this helps at least one engineer to become more fluent in the world of robotics, means a lot to me! 🫶🏼 Here's the course (it's free): https://lnkd.in/dRYdkmdi ~~ ♻️ Join the weekly robotics newsletter, and never miss any news → ziegler.substack.com

Exciting updates on Project GR00T! We discover a systematic way to scale up robot data, tackling the most painful pain point in robotics. The idea is simple: human collects demonstration on a real robot, and we multiply that data 1000x or more in simulation. Let’s break it down: 1. We use Apple Vision Pro (yes!!) to give the human operator first person control of the humanoid. Vision Pro parses human hand pose and retargets the motion to the robot hand, all in real time. From the human’s point of view, they are immersed in another body like the Avatar. Teleoperation is slow and time-consuming, but we can afford to collect a small amount of data.  2. We use RoboCasa, a generative simulation framework, to multiply the demonstration data by varying the visual appearance and layout of the environment. In Jensen’s keynote video below, the humanoid is now placing the cup in hundreds of kitchens with a huge diversity of textures, furniture, and object placement. We only have 1 physical kitchen at the GEAR Lab in NVIDIA HQ, but we can conjure up infinite ones in simulation. 3. Finally, we apply MimicGen, a technique to multiply the above data even more by varying the *motion* of the robot. MimicGen generates vast number of new action trajectories based on the original human data, and filters out failed ones (e.g. those that drop the cup) to form a much larger dataset. To sum up, given 1 human trajectory with Vision Pro  -> RoboCasa produces N (varying visuals)  -> MimicGen further augments to NxM (varying motions). This is the way to trade compute for expensive human data by GPU-accelerated simulation. A while ago, I mentioned that teleoperation is fundamentally not scalable, because we are always limited by 24 hrs/robot/day in the world of atoms. Our new GR00T synthetic data pipeline breaks this barrier in the world of bits. Scaling has been so much fun for LLMs, and it's finally our turn to have fun in robotics! We are creating tools to enable everyone in the ecosystem to scale up with us: - RoboCasa: our generative simulation framework (Yuke Zhu). It's fully open-source! Here you go: http://robocasa.ai - MimicGen: our generative action framework (Ajay Mandlekar). The code is open-source for robot arms, but we will have another version for humanoid and 5-finger hands: https://lnkd.in/gsRArQXy - We are building a state-of-the-art Apple Vision Pro -> humanoid robot "Avatar" stack. Xiaolong Wang group’s open-source libraries laid the foundation: https://lnkd.in/gUYye7yt - Watch Jensen's keynote yesterday. He cannot hide his excitement about Project GR00T and robot foundation models! https://lnkd.in/g3hZteCG Finally, GEAR lab is hiring! We want the best roboticists in the world to join us on this moon-landing mission to solve physical AGI: https://lnkd.in/gTancpNK

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!

4-DOF Dual Robotic Arm Pick & Place Simulation in MATLAB ➡ Coordinated dual-arm manipulation for cubes, spheres, and cylinders ➡ Analytical Inverse Kinematics for fast and accurate joint computation ➡ DH-parameter-based kinematic modeling ➡ Smooth trajectory planning with multi-stage interpolation ➡ Real-time 3D visualization with end-effector path tracing ➡ Automated simulation video generation ✨ Why this matters: In robotics, dual-arm coordination is crucial for industrial automation, collaborative robots, and intelligent material handling. This simulation demonstrates how accurate kinematics, workspace-safe IK, and trajectory planning enable two manipulators to work together seamlessly in a 3D environment. Beyond visualization, the project reinforces core concepts in joint coordination, kinematic modeling, and end-effector path planning, making it highly valuable for academic learning, prototyping, and portfolio building. 📊 Key Highlights: ✔ Dual 4-DOF manipulators working collaboratively ✔ Analytical IK for precise motion and stability ✔ Real-time 3D animation with labeled joints and links ✔ Smooth multi-stage trajectory interpolation ✔ Workspace-safe motion planning ✔ Supports multiple object shapes (cube, cylinder) 💡 Future Potential: This framework can be extended toward: ➡ Dynamic modeling & torque-based control ➡ Obstacle avoidance & path optimization ➡ ROS integration for real-world deployment ➡ AI-based trajectory planning and reinforcement learning 🔗 For students, engineers & robotics enthusiasts: This is a ready-to-use MATLAB project for learning, teaching, and prototyping advanced dual-arm robotic systems. 🔁 Repost to support robotics learning & engineering innovation! 🔁 #Robotics #MATLAB #Automation #4DOF #RobotArm #Kinematics #TrajectoryPlanning #InverseKinematics #ForwardKinematics #PickAndPlace #ControlSystems #Mechatronics #EngineeringProjects #Simulation #3DAnimation #STEM #RoboticsEngineering #TechInnovation

NVIDIA researchers are using the Apple Vision Pro headset to control humanoid robots in real-time. Imagine putting on a headset and suddenly feeling as if you're inside a robot's body, controlling its movements with your own. According to the researchers, that's exactly the experience - they describe it as feeling "immersed" in another body, much like the movie Avatar. 𝐒𝐨, 𝐇𝐨𝐰 𝐃𝐨𝐞𝐬 𝐓𝐡𝐢𝐬 𝐖𝐨𝐫𝐤? Let me break it down: - Human Demonstration with Apple Vision Pro: Operators wear the Apple Vision Pro headset to control humanoid robots. This provides initial demonstration data as they perform tasks the robot needs to learn. - RoboCasa Simulation Framework: This is a simulation tool that takes the real-world data from the human demonstrations and multiplies it by generating a variety of virtual environments. Think of it as creating numerous practice scenarios without needing more human input. - MimicGen Data Augmentation: Building on that, MimicGen creates new robot motion paths based on the human demonstrations. It's like giving the robot creativity to try new ways of performing tasks. - Quality Filtering: The system automatically filters out any failed attempts, ensuring the robot learns only from successful actions. This process turns limited human input into a vast, high-quality dataset. 𝐖𝐡𝐲 𝐈𝐬 𝐓𝐡𝐢𝐬 𝐚 𝐁𝐢𝐠 𝐃𝐞𝐚𝐥? Traditionally, training robots requires a lot of human time and effort, which can be expensive and slow. NVIDIA's approach can multiply robot training data by 1,000 times or more using simulations. By leveraging powerful GPUs (graphics processing units), researchers can substitute computational power for costly human labor. Just as large language models (like those behind advanced chatbots) have rapidly improved by scaling up training data, this method could lead to advances in robot capabilities and adaptability. We're talking about robots that can learn and adapt much more quickly than before. The ability to efficiently scale training data means we could see rapid advancements in how robots perform complex tasks, interact with environments, and maybe even integrate into our daily lives sooner than we thought. Do you see this as a step forward in robotics and AI? How might this impact the future of work and technology? #innovation #technology #future #management #startups

Simulation lets us train millions of trajectories. Reality tests whether any of them actually work. The Sim2Real gap isn’t one problem — it’s three: 1️⃣ Visual Shift (Real ≠ Synthetic) Real scenes have noise, clutter, glare, shadows, messy backgrounds. Sim rarely does. 2️⃣ Physics Shift (Approximation ≠ Reality) Small errors in friction, damping, mass, or latency → huge drift in behavior. 3️⃣ Embodiment Shift (Robot ≠ Robot-in-Sim) Morphology, joint limits, actuator dynamics — nothing matches perfectly. What works today? • Domain Randomization — vary textures, lights, physics, noise until the policy generalizes by force • Domain Adaptation — align real + sim feature distributions • System Identification — tune sim from real sensor measurements • Real-to-Sim Feedback Loops — use a tiny amount of real data to anchor the model As robotics foundation models scale, most of their data will come from simulation. Teams who master domain adaptation will be the ones who can actually deploy these models on physical robots — not just in demos. I added my favorite papers, frameworks, and tools in the comments 👇

Big shift in robotics: NVIDIA just open-sourced Isaac Sim and Isaac Lab. Isaac Sim has already been a cornerstone for high-fidelity robotics simulation—RTX-accelerated physics, realistic lidar/camera simulation, domain randomization, ROS/URDF support, and synthetic data pipelines. Now, it’s all on GitHub with full source access. But the real multiplier? The release of Isaac Lab—a modular, open reinforcement learning and robot control framework built directly on top of Isaac Sim. It comes with ready-to-use robots (Franka, UR5, ANYmal), training loops, and environments for manipulation, locomotion, and more. What’s different now: *You’re no longer limited to APIs—developers can modify physics, sensors, and control logic at the source level. *Isaac Lab provides a training-ready foundation for sim-to-real robotics, speeding up learning pipelines dramatically. *Debugging, benchmarking, and custom integrations are now transparent, flexible, and community-driven. *Collaboration across research and industry just got easier—with reproducible environments, tasks, and results. We’ve used Isaac Sim extensively, and this open-source release is going to accelerate innovation across the robotics community. GitHub: https://lnkd.in/gcyP9F4H

How Can Gen AI Revolutionize Robot Learning? MIT’s Computer Science and AI Lab (CSAIL) has unveiled a promising breakthrough in robotics training—LucidSim, a system powered by generative AI that could help robots learn complex tasks more efficiently. Traditionally, robots have struggled with a lack of training data—but LucidSim taps into the power of AI-generated imagery to create diverse, realistic simulations. By combining text-to-image models, physics simulations, and auto-generated prompts, LucidSim can rapidly produce large amounts of training data for robots—whether it’s teaching them to navigate parkour-style obstacles or chase a soccer ball. This system outperforms traditional methods like domain randomization and even human expert imitation in many tasks. Key takeaways: - Generative AI is being used to scale up data generation for robotics training, overcoming the industry’s current data limitations. - LucidSim has shown strong potential for improving robot performance and pushing humanoid robots toward new levels of capability. - Researchers aim to improve robot learning and general intelligence to help robots handle more real-world challenges. With robots continuing to grow in sophistication, this innovative approach could mark a significant step toward more capable, intelligent machines in the future!

Hello 👋 from the Automate Show in downtown Detroit. I’m excited to share with you what I’m learning. Robotics is undergoing a fundamental transformation, and NVIDIA is at the center of it all. I've been watching how leading manufacturers are deploying NVIDIA's Isaac platform, and the results are staggering: Universal Robotics & Machines UR15 Cobot now generates motion faster with AI. Vention is democratizing machine motion for businesses. KUKA has integrated AI directly into their controllers. But what's truly revolutionary is the approach: 1. Start with a digital twin In simulation, companies can deploy thousands of virtual robots to run experiments safely and efficiently. The majority of robotics innovation is happening in simulation right now, allowing for both single and multi-robot training before real-world deployment. 2. Implement "outside-in" perception Just as humans perceive the world from the inside out, robots need their own sensors. But the game-changer is adding "outside-in" perception - like an air traffic control system for robots. This dual approach is solving industrial automation's biggest challenges. 3. Leverage generative AI Factory operators can now use LLMs to manage operations with simple prompts: "Show me if there was a spill" or "Is the operator following the correct assembly steps?" Pegatron is already implementing this with just a single camera. They're creating an ecosystem where partners can integrate cutting-edge AI into existing systems, helping traditional manufacturers scale up through unprecedented ease of use. The most powerful insight? Just as ChatGPT reached 100 million users in 9 days, robotics adoption is about to experience its own inflection point. The barriers to entry are falling. The technology is becoming accessible even for mid-sized and smaller companies. And the future is being built in simulation before transforming our physical world. Michigan Software Labs Forbes Technology Council Fast Company Executive Board

Burning out an servo amplifier hurts. Both financially and professionally. You can have perfect ladder logic, but you can’t cheat physics. In Motion Control, an error rarely ends with a simple red "Error" LED. It usually ends with a bang. So, how do you master drives and encoders without risking the demo gear? Forget "trial and error" on live machinery. Leverage the software you already have: ➡️ Simulation is more than just logic. Many engineers use the simulator only to test bits. Big mistake. In the MELSOFT environment, you can simulate the entire axis behavior. Watch the Current Feedback during hard braking. A virtual collision costs $0. ➡️ Trace Monitor is your eyes. Stop guessing why the motor is "buzzing." Fire up the built-in oscilloscope. If you can’t read a Speed vs. Torque graph, you aren’t controlling the machine - you’re just hoping it works. ➡️ Start with the Compact PLC. You don’t need a massive PLC cabinet to learn. The Compact PLC comes with built in positioning features. Master ramps, Jerk, and Homing on your desk, in a safe micro-scale. A pro engineer doesn’t pray for the machine to run. They KNOW it will run because they verified it before plugging in the CC-Link IE TSN cable. 👇 Team "Adrenaline & Live Testing" or Team "Simulation First"? Let me know in the comments.