Credit: https://oneplm.com/case-studies/autotech/

In 1997, a tiny robot named Sojourner made history by roving across the Martian surface, capturing images and analyzing rocks, all while being millions of miles away from human hands. What made this feat possible wasn’t just the hardware strapped to the little rover, but countless hours of simulation back on Earth. Before Sojourner ever touched Martian soil, every wheel turn and arm movement was tested virtually. This is the often-unsung power of simulation in robotics; it lets us explore, test, and innovate long before a real robot hits the ground.

Robotics is a blend of mechanical engineering, electronics, and Computer Science. Making all these elements work together seamlessly in the physical world is no small feat. Simulation software offers a safe and cost-effective way to prototype and test robotic systems in a virtual environment. Whether you’re programming a robotic arm to assemble delicate electronics or building an autonomous drone to navigate a forest, simulations allow you to spot errors, fine-tune algorithms, and validate your designs without risking expensive hardware. It’s like having a digital playground where trial and error comes without consequence.

But simulation is not just about safety, it’s also about speed and creativity. With simulation tools, engineers and students can run multiple test scenarios in parallel, speed up or slow down time, and even simulate real-world physics like friction, gravity, and sensor noise. This accelerates the development process and opens the door to experimentation that would be impossible or impractical in real life. In essence, simulation turns imagination into action, bridging the gap between an idea scribbled on paper and a robot navigating the real world.

In this article, we are going to explore popular simulation software which are used to simulate robotic systems. Let the journey begin!

1) Gazebo

Gazebo is the most popular open-source simulation software for Robotics. You are very likely to come across this software when exploring the world of simulators, and it’s for a reason, so let’s discuss some pros and cons of Gazebo.

Pros

• Strong integration with ROS
• Realistic Physics Engine
• Open-Source and Free to Use
• Can Simulate Various Sensors
• Extensive Community Support
• Highly Versatile and Customizable

Cons

• Resource Intensive, Requiring Powerful Hardware to Run Simulations Smoothly
• Steep Learning Curve
• Occasional Bugs and Glitches
• Simulations do not Look Visually Realistic

However, the benefits far outweigh the downsides. Gazebo’s active community, rich documentation, and consistent updates make it a long-time favorite in both classrooms and research labs. For anyone serious about robotics, whether you’re building your first line-following bot or testing autonomous drones, Gazebo is a must-have ally in your journey.

2) NVIDIA Isaac Sim

Credit: https://siliconangle.com/2023/01/03/nvidia-unveils-advances-autonomous-robotics-simulation-using-ai/

NVIDIA Isaac Sim is designed for AI-based robotics development, especially in scenarios that involve perception, manipulation, and synthetic data generation. It supports real-time physics with PhysX, beautiful ray-traced rendering, and tight integration with ROS, ROS2, and Python APIs. You can simulate robots in photorealistic 3D environments, generate labeled datasets for training deep learning models, and even test robot algorithms with extreme precision.

Pros

• Photorealistic Rendering: Stunning Graphics
• GPU Accelerated Physics Powered by NVIDIA GPUs
• Synthetic Data Generation – Automatically generates labeled datasets (bounding boxes, segmentation masks, depth maps, etc.) for machine learning, saving time.
• Seamless ROS Integration
• Modular and Scalable – Supports multi-robot simulations, cloud deployment, and digital twin setups for large-scale industrial robotics.
• Built with AI workflows in mind: ideal for training and testing deep learning algorithms in high-fidelity virtual environments.

Cons

• Requires High-End Hardware – Demands a powerful NVIDIA RTX GPU and lots of memory.
• Complex Setup Procedure
• Steep Learning Curve

Isaac Sim facilitates three essential workflows: generating synthetic data for training and fine-tuning robot foundation models, conducting software-in-the-loop testing for robot stacks, and enabling robot learning through Isaac™ Lab.

Credit: https://developer.nvidia.com/isaac/sim

NVIDIA Isaac Sim is a dream tool for AI/robotics researchers, advanced developers, and industries building next-gen robots that rely heavily on perception, manipulation, and real-world interaction, think warehouse automation, robotic arms, or autonomous drones. It’s especially powerful for those working with computer vision, synthetic data generation, and deep learning, offering photorealistic environments and GPU-accelerated physics. But for beginners just dipping their toes into robotics, Isaac Sim might feel like jumping into the deep end of the pool; it’s incredibly capable, but comes with a steep learning curve and serious hardware requirements. If you’re a beginner with big ambitions (and a solid GPU), it can be a thrilling ride, but if you’re just starting out, something like Gazebo or Webots might be a smoother first step.

3) Webots

For those beginning their journey in robotics, Webots serves as an accessible and well-rounded simulation platform. It is an open-source tool designed to simplify the process of building, programming, and testing robotic systems in a virtual environment. Webots supports popular programming languages such as Python, C++, Java, and MATLAB, allowing users to develop and simulate robotic behaviors. Its intuitive interface, extensive library of prebuilt robot models, and realistic 3D environments make it particularly suitable for students, educators, and early-stage researchers seeking a practical introduction to robotics simulation.

Despite its beginner-friendly nature, Webots is also equipped with advanced features that make it suitable for more complex and academic applications. It offers support for various sensors, including cameras, LIDARs, GPS, and IMUs, and includes a physics engine capable of realistic simulation of robot-environment interactions. Webots also integrates well with ROS, enabling seamless transition to real-world robotic systems. Its ability to simulate multi-robot systems, support real-time control, and generate reproducible results positions it as a valuable tool not only for learning but also for conducting meaningful research and prototyping in the field of robotics.

Pros

• Free and Open-Source
• User-Friendly Interface
• Photo-Realistic Simulations
• Wide Range of Sensors and Actuators
• Multi-language Support – Allows programming in Python, C++, Java, MATLAB, and more.
• ROS Integration
• Good Performance – especially in terms of resource usage.
• Cross-Platform Support – Available for Windows, macOS, and Linux
• Fast Iteration and Testing
• Actively maintained

Cons

• Documentation Limitations: While the documentation is comprehensive, it may stop at simple topics, potentially leaving users with more complex issues without adequate guidance. 
• Relatively Less Extensive Community Support as compared to other popular simulators.
• Less commonly used in large-scale industrial applications compared to Gazebo or Isaac Sim.
• Graphics Not as Advanced: While functional, the visuals are not as high-fidelity or modern-looking as simulators like Unity or Isaac Sim.

Webots is ideal for students, educators, and early-stage robotics researchers who need an easy-to-use yet powerful simulation tool for learning, prototyping, and algorithm development. Its intuitive interface, multi-language support, and built-in robot models make it especially suitable for those new to robotics, while its ROS integration and realistic physics also appeal to more advanced users conducting academic research or rapid testing.

4) CoppeliaSim

CoppeliaSim, formerly known as V-REP, is a powerful and flexible robot simulation platform designed for modeling, simulating, and controlling a wide variety of robotic systems. What makes CoppeliaSim stand out is its scene-based architecture, where you can simulate complex environments with multiple robots interacting simultaneously. It supports a wide range of sensors, actuators, and physics engines, and allows scripting using Lua, or controlling robots externally via Python, C++, Java, MATLAB, and even over TCP/IP or ROS. This flexibility makes it an excellent choice for both academic research and industrial prototyping.

CoppeliaSim is especially useful for simulating multi-robot systems, robot arms, and swarm robotics, thanks to its real-time capabilities and built-in path planning tools. It comes with a vast library of models and tools for tasks like motion planning, inverse kinematics, vision processing, and more. Its modularity and scripting options provide unmatched control and customization, making it a favorite among experienced researchers and developers working on complex robotic simulations.

Pros

• Highly Flexible & Customizable
• Excellent Multi-Robot & Swarm Simulation
• Free Educational License
• Supports ROS
• Cross-Platform and Open-Source

Cons

• Steep Learning Curve
• Complex and Outdated User Interface
• Limited ROS2 Integration – While ROS1 is well supported, ROS2 integration isn’t as mature as in simulators like Gazebo.

CoppeliaSim is best suited for robotics researchers, developers, and advanced students who need a flexible and feature-rich environment for simulating complex robotic systems. It’s particularly valuable for those working on multi-robot coordination, robotic manipulators, or swarm robotics, thanks to its powerful APIs, scripting capabilities, and built-in tools like motion planning and inverse kinematics. While it may not be the easiest tool for beginners, those with a solid foundation in robotics and programming will appreciate the level of control and customization it offers for both academic and industrial simulation projects.

5) MATLAB and Simulink

Credit: https://blogs.mathworks.com/student-lounge/2019/09/25/robotics-r2019b/

MATLAB and Simulink provide a robust ecosystem for simulating, designing, and testing robotic systems with precision and efficiency. MATLAB, known for its numerical computing capabilities, allows users to develop algorithms for perception, control, path planning, and sensor fusion with ease. Simulink complements this by offering a graphical, model-based design environment, where users can visually connect system components, simulate their behavior in real time, and adjust parameters on the fly. This integration is particularly beneficial in robotics, where complex control logic, sensor data, and dynamic system modeling must work seamlessly together.

What sets MATLAB and Simulink apart in robotics is their extensive toolbox support, such as the Robotics System Toolbox, Computer Vision Toolbox, and Simscape Multibody, which allow users to simulate everything from mobile robots and manipulators to autonomous vehicles and drones. The platform also supports ROS and ROS2, enabling direct interaction between simulated models and real robots. With capabilities for hardware-in-the-loop (HIL) testing, automatic code generation, and integration with AI workflows, MATLAB and Simulink provide a professional-grade simulation environment.

Pros

• Integrated Environment: Seamless connection between algorithm development (MATLAB) and system-level simulation (Simulink) allows for a smooth, end-to-end robotics workflow.
• Rich Toolboxes for Robotics
• Model-Based Design: Simulink’s block-diagram approach is intuitive for visualizing complex systems, especially useful for control logic, mechatronics, and multi-domain simulations.
• Hardware Integration: Supports hardware-in-the-loop (HIL) testing and automatic code generation for deployment on real hardware, including ROS, Arduino, Raspberry Pi, and embedded systems.
• Strong Documentation & Support: Excellent learning resources, documentation, and active support from MathWorks and the academic community.

Cons

• Expensive Licensing
• Closed Ecosystem: MATLAB is a proprietary platform, which limits open-source collaboration and flexibility
• High Computational Demand

MATLAB and Simulink are best suited for researchers, engineers, and advanced students who need a highly reliable, feature-rich environment for modeling, simulation, and hardware integration. They are especially valuable in academic institutions, R&D labs, and industrial settings where precision, validated algorithms, and rapid prototyping are critical. MATLAB and Simulink are ideal for users aiming to transition smoothly from simulation to real-world deployment. Those working on complex systems like autonomous vehicles, robotic manipulators, or sensor fusion applications will particularly benefit from their robust toolboxes and professional-grade capabilities.

6) Habitat Lab

Credit: https://siliconangle.com/2021/06/30/facebook-makes-habitat-virtual-world-ai-training-environment-interactive/

Meta’s Habitat Lab is a cutting-edge simulation platform designed to advance the field of Embodied AI, where intelligent agents learn by interacting with their environments. At its core, Habitat enables researchers to train virtual robots in highly realistic 3D simulations, allowing them to develop skills like navigation, object manipulation, and human interaction before deploying these capabilities in the real world. This approach addresses the challenges of real-world training, which can be slow, costly, and potentially hazardous. ​

The platform comprises two main components: Habitat-Sim, a high-performance 3D simulator, and Habitat-Lab, a modular library for defining tasks, configuring agents, and benchmarking performance. Habitat 3.0, the latest iteration, introduces advanced features such as accurate humanoid simulation, human-in-the-loop interaction, and collaborative tasks like social navigation and rearrangement. These enhancements make Habitat a powerful tool for studying complex human-robot interactions in home-like environments. ​

Pros

• High-Speed, Photorealistic Simulation
• Support for Complex Embodied AI Tasks: Excellent for navigation, object rearrangement, and social interaction tasks, with support for 3D indoor environments and interactive agents.
• Modular and Scalable: Can simulate multiple agents and tasks using a flexible configuration system, making it ideal for benchmarking and large-scale experimentation.
• Integration with Habitat-Lab: Comes with a powerful companion library for defining environments, tasks, and evaluation metrics, streamlining the entire training pipeline.
• Active Research Community

Cons

• Steep Learning Curve
• Limited Use Outside Embodied AI: Primarily designed for research in navigation and manipulation tasks in indoor environments, less suitable for mobile robotics or industrial simulation.
• Hardware Intensive
• Lacks Real-World Physics Complexity: While great for agent training, it doesn’t simulate physics at the same depth as platforms like Gazebo or Isaac Sim for contact dynamics or mechanical modeling.

For researchers and developers focused on embodied AI, Habitat Lab offers a robust and flexible environment to prototype, test, and refine intelligent agents capable of operating in dynamic, real-world settings.

Exploring CodeRobo.AI Simulation Platform

Imagine a world where kids can dive into the exciting universe of coding and robotics without needing any wires, batteries, or expensive hardware. CodeRobo.AI makes that world a reality. It’s an online platform designed for educators and parents to introduce children to the fundamentals of programming and robotics using a charming, interactive robot in a 3D simulation. With the drag-and-drop simplicity of Blockly, even young learners can easily write code to control their robot and complete fun, engaging missions, fostering problem-solving and critical thinking skills from the very start. CodeRobo also offers controlling the robot using Python script which introduces the children to coding and programming logic.

What truly sets CodeRobo apart is its immersive 3D environment, crafted to reflect real-life spaces kids can relate to. The robot operates in a colorful and friendly world filled with familiar objects, making the learning experience feel playful rather than intimidating. As students progress through missions and challenges, they build confidence and a sense of accomplishment, all while mastering real programming logic. Its education wrapped in adventure, where each completed task is another step toward becoming a young engineer or coder.

Pros

• User-Friendly Interface
• Beginner Friendly
• Web-based Application
• Python and Blockly Support
• Realistic Physics Engine
• Simulate Range Based Sensors
• Hands-on Courses with Interesting Missions
• Regular Updates and Improvements

Cons

• Limited Complexity
• Lack of Customization
• No Hardware Integration

CodeRobo is not just fun, it’s practical and accessible. Since everything runs on a web browser, there’s no need for physical robots or software installations, making it affordable and scalable for classrooms and homes alike. Behind the scenes, the platform is built by a team of seasoned software engineers and roboticists, ensuring the content reflects real-world skills in a kid-friendly package. Whether you’re an experienced educator or a curious parent, CodeRobo offers an easy and inspiring way to light the spark of STEM in young minds, anytime, anywhere.

Check out the courses at CodeRobo.AI to Get Started!

Conclusion

Simulators are a great tool for testing robots and algorithms in different environments, which might be complex or costly to test in the real world using actual hardware or robots. While working on any robotics project, simulators are an essential tool for rapid iteration and refinement without the fear of damaging hardware or endangering living beings. It’s always a smart and responsible approach to validate your systems in simulation first, ensuring stable and expected behavior before transitioning to real-world deployment.

If you are starting your journey into robotics simulation, CodeRobo.AI is an amazing and easy-to-use platform for all age groups to gain valuable insights and have an enjoyable learning experience.