Robotics Simulation Programs

Release of Genesis represents something extraordinary. After diving deep into the research paper, I want to share why this isn't just another AI tool - it's potentially the bridge to making personal robots a reality. What is Genesis?  Imagine having a "virtual universe" where robots can practice tasks millions of times in minutes, learning from each experience, all before attempting anything in the real world. That's Genesis - but it's even more fascinating than that. 🔄 The Traditional vs Genesis Approach Let me share a simple example that blew my mind: Teaching a robot to pour water traditionally: - Program every movement manually - Test with real water (risking robot damage) - Repeat thousands of times - Limited to specific cups and situations learned With Genesis: Simply tell it: "Pour water from a pitcher into a cup without spilling" Genesis automatically: - Tests different cup sizes and shapes - Varies water amounts and conditions - Adjusts for different surfaces - Completes millions of practice runs in hours And here's the kicker - it runs 430,000 times faster than real-time! What would take a year to learn traditionally can be learned in 45 seconds. 🤯 🎮 Four Game-Changing Components: 1. Universal Physics Engine - Simulates at 43 million frames per second - 430,000x faster than real-time operation - Accurate physics for multiple material types in one simulation 2. Ultra-Fast Robotics Platform - Processes 1 year of training in 45 seconds - Enables parallel testing of thousands of scenarios 3. Photo-Realistic Rendering - Real-time physics-based rendering - Accurate material and lighting simulation 4. Natural Language Understanding - Converts plain English to robot commands - Handles complex multi-step instructions 💡 Why This Matters: Think about how we currently develop robots - it's like teaching someone to swim without water. Genesis changes this by creating a perfect practice environment where: - Engineers can test wild ideas without physical prototyping - Robots can learn complex tasks through millions of attempts 🌍 Beyond Robotics - Universal Applications: Genesis isn't just for robotics - it's transforming multiple fields: - Healthcare: Medical robots practicing surgical procedures millions of times before touching a patient - Architecture: Building design and structural analysis - Entertainment: Physics-accurate animations and VR - Education: Interactive learning environments - Manufacturing: Manufacturing robots reconfiguring for new tasks through simple instructions 🔮 Future Vision: Imagine describing a task to your home robot in plain language, and it understanding exactly what to do because it's already practiced similar scenarios millions of times in simulation. That future just got much closer. #AI #Robotics #Innovation #TechnologyInnovation #FutureOfWork #ArtificialIntelligence #RoboticAutomation

Exciting News! R2024a Release Highlights for MathWorks Robotics Toolboxes! 🤖 MATLAB and Simulink enthusiasts, get ready to explore a wealth of new and enhanced features in the latest release!   🔹 Robotics System Toolbox: - High-Fidelity 3-D Simulation: Develop, test, and verify robotics algorithms in a 3-D simulation environment rendered using Unreal Engine from Epic Games - Train CHOMP optimizer for more faster motion planning using deep learning - Connect and control Universal Robots’ cobots using ROS 2. - Learn More: https://lnkd.in/esPEswgR   🔹 UAV Toolbox: - Design and deploy flight controllers for vertical take-off and landing (VTOL) UAVs. - PX4 Autopilots: Interface seamlessly with PX4 Cube Orange Plus and Pixhawk 6c autopilots. - Learn More: https://lnkd.in/ecz-gK9s     🔹 Navigation Toolbox: - Deep-Learning-Based Path Planner: Perform motion planning using Motion Planning Networks (MPNet) - Camera-to-IMU Extrinsic Calibration: Estimate transformation between camera and IMU sensors. - Learn More: https://lnkd.in/expvK666     🔹 ROS Toolbox: - ROS Data Analyzer App: Play and visualize live data from topics in ROS and ROS 2, and add tags and search bag files - Generate and deploy C++ code to build custom ROS 2 messages directly on target hardware using MATLAB Coder and Simulink Coder - Learn More: https://lnkd.in/epnDjNAe     Explore these updates and more in the full release notes. 👉 Watch the R2024a Release Highlights video to stay ahead in the robotics game! https://lnkd.in/ehD57TH4 #Robotics #MATLAB #Simulink #R2024aRelease #Innovation

How can we bridge the gap between simulation and reality in robotics? Developed by a team from UC Berkeley, Google DeepMind, and other leading institutions, MuJoCo Playground is a fully open-source framework revolutionizing robotic learning and deployment. This tool enables rapid simulation, training, and 𝘇𝗲𝗿𝗼-𝘀𝗵𝗼𝘁 𝘀𝗶𝗺-𝘁𝗼-𝗿𝗲𝗮𝗹 𝘁𝗿𝗮𝗻𝘀𝗳𝗲𝗿 across diverse robotic platforms. MuJoCo Playground supports quadrupeds, humanoids, dexterous hands, and robotic arms, train reinforcement learning policies in minutes on a single GPU, and streamline vision-based and state-based policy training with integrated batch rendering and a powerful physics engine. The framework’s real-world success is evidenced by its deployment on platforms like Unitree Go1, LEAP hand, and the Franka arm within 8 weeks. Its efficiency and simplicity empower researchers to focus on innovation. A simple 'pip install playground' will do! Congratulations to the team, Kevin Zakka, Baruch Tabanpour, Qiayuan Liao, Mustafa Haiderbhai, Samuel Holt, Carmelo (Carlo) Sferrazza, Yuval Tassa, Pieter Abbeel and collaborators, for this game-changing contribution to robotics! 🔗 Check out their website here https://lnkd.in/g7mbZtXg for their paper, github, live demo, and even a google colab setup for an easy start! 💬 What do you think is the next big challenge for sim-to-real transfer in robotics? Let's discuss below! P.S. Excited to share an open source framework I've been experimenting with recently! #Robotics #AI #Simulation #MachineLearning #Engineering #Innovation #ReinforcementLearning 

🚀 Robot Operating System (#ROS) is getting a new standard for simulation, which I am proud to have authored. Why does it matter for the robotics community? There are multiple simulators with ROS integrations. Each has unique strengths; none is best for everyone. Still, the way they are used is often very similar. The new standard makes it easier to build integrations, switch between simulators, or even use multiple simulators in parallel. The standard defines highly useful features such as spawning robots and other objects, moving things around for testing, stepping through simulation and querying the virtual world for ground truth data. It is supportive of automation and scenario-based testing. Huge thanks to veteran members of the ROS community at large as well as leads for Open Robotics Gazebo, NVIDIA Isaac Sim and Open 3D Engine for co-authoring this through high-effort, engaged review process. These three simulators will be the first to adopt the standard in upcoming releases. Some changes are still expected as it is being implemented - preview it here: https://lnkd.in/dr6SZnaB. #ros2 #simulation #robotics

University of Michigan Researchers Introduce OceanSim: A High-Performance GPU-Accelerated Underwater Simulator for Advanced Marine Robotics Researchers from the University of Michigan have proposed OceanSim, a high-performance underwater simulator accelerated by NVIDIA parallel computing technology. Built upon NVIDIA Isaac Sim, OceanSim leverages high-fidelity, physics-based rendering, and GPU-accelerated real-time ray tracing to create realistic underwater environments. It bridges underwater simulation with the rapidly expanding NVIDIA Omniverse ecosystem, enabling the application of multiple existing sim-ready assets and robot learning approaches within underwater robotics research. Moreover, OceanSim allows the user to operate the robot, visualize sensor data, and record data simultaneously during GPU-accelerated simulated data generation. OceanSim utilizes NVIDIA’s powerful ecosystem, providing real-time GPU-accelerated ray tracing while allowing users to customize underwater environments and robotic sensor configurations. OceanSim implements specialized underwater sensor models to complement Isaac Sim’s built-in capabilities. These include an image formation model capturing water column effects across various water types, a GPU-based sonar model with realistic noise simulation for faster rendering, and a Doppler Velocity Log (DVL) model that simulates range-dependent adaptive frequency and dropout behaviors. For imaging sonar, OceanSim utilizes Omniverse Replicator for rapid synthetic data generation, establishing a virtual rendering viewport that retrieves scene geometry information through GPU-accelerated ray tracing..... Read full article: https://lnkd.in/gjTAkB2b Paper: https://lnkd.in/gEhq-SNQ