Before you burn out a motor, crash a drone, or launch your robot off a table, there's a safer way to test your code.
There are a lot of robot simulators out there, they all claim to do everything, and half the tutorials you'll find are three ROS versions out of date. If you're working with ROS 2, picking the right simulator early saves you a lot of wasted effort. This is a plain-English tour of the six robot simulators worth knowing, and which one actually fits your project.
Why simulate at all?
Hardware is expensive, slow, and fragile. A simulator lets you test faster, break things safely, and iterate in minutes instead of hours. You can crash a robot a thousand times in sim and the only cost is a restart.
If you're learning ROS 2, starting in simulation is the smarter path. You get the full sense, think, act loop working in software first, which is the same idea we broke down in What Even Is a Robot?. Here's how the main options stack up.
Gazebo: the ROS 2 default
Gazebo is the one everyone mentions first, for good reason. It's effectively the default for ROS robotics, with solid physics, sensor modeling, and a huge library of robot models. If you're on ROS 2, you're most likely using Gazebo Sim, the modern version (Harmonic is the current long-term release, formerly known as Ignition).
The downside is that setup can be rough, and the first hour often goes to wiring things together rather than building. But for mobile robots, manipulators, and most ROS workflows, Gazebo is still the baseline. It's what we used to build a mobile robot simulation from a single prompt and then add a LiDAR sensor.
Webots: the easiest place to start
Webots is what you reach for when Gazebo feels overwhelming. It's open source, cross-platform, and much easier to get running. No ROS is required, though ROS 2 support is there through the webots_ros2 package if you want it, and it ships with a large library of ready-made robot models.
The physics aren't as advanced as Gazebo's, but for education, quick prototypes, and most beginner projects, that honestly won't matter. If your goal is to learn and move fast, Webots gets you to a running robot with the least friction.
Isaac Sim: the choice for AI and perception
If Gazebo is the workhorse, NVIDIA's Isaac Sim is the cinematic universe. Built on NVIDIA Omniverse, it's photorealistic, physically accurate, and deeply tied to AI workflows: synthetic data generation, domain randomization, and sim-to-real transfer.
The catch is that you'll need a strong NVIDIA GPU, and the learning curve is steeper. But for perception and AI-heavy robotics, where visual fidelity directly affects whether your trained models work in the real world, nothing else comes close.
CoppeliaSim: dependable for arms and academia
Previously called V-REP, CoppeliaSim has been quietly reliable for years. It's especially strong for robotic arms and multi-robot simulations, with built-in scripting, ROS support, and the ability to swap between physics engines. It also runs surprisingly well on modest hardware.
It's dependable rather than flashy: stable, flexible, and widely used across academia for exactly that reason.
Reinforcement learning simulators: PyBullet and MuJoCo
These two come up constantly in reinforcement learning and robotics research.
PyBullet:
It is lightweight, Python-friendly, and easy to set up. If you want to train RL agents without spending hours configuring a simulator, PyBullet is a great place to start. It has no official ROS 2 bridge, so it's better suited to pure learning experiments than full-stack testing.
MuJoCo:
It sits at the other end of the spectrum. MuJoCo is the simulator behind a huge amount of modern robotics research, especially humanoids, robotic manipulation, and legged locomotion. If you've seen a robot hand solving a Rubik's Cube, a humanoid learning to walk, or an arm trained to follow a human hand, there's a good chance MuJoCo was somewhere in the pipeline. It's fast, accurate, now open source, and trusted enough that it became the go-to for many RL researchers.
How to choose the right robot simulator
Match the tool to the job. For full-stack ROS 2 work like navigation and SLAM, start with Gazebo. If you're new and want momentum, Webots. For AI, vision, and synthetic data, Isaac Sim. For robotic arms and reproducible academic work, CoppeliaSim. For reinforcement learning, PyBullet to learn and MuJoCo for serious research.
Whichever you pick among the ROS 2 options, the slow part is usually the same: standing up the workspace, the robot description, and the sensor and controller configuration before you can run anything. That setup tax is exactly what Drift is built to remove. It generates production-grade simulation workspaces for ROS 2 across Gazebo, MuJoCo, and Isaac Sim from a single prompt, so you spend your time on the robotics instead of the plumbing.
The bottom line
There's no single best robot simulator, only the best one for what you're building. Gazebo for ROS 2 by default, Webots to learn, Isaac Sim for AI and perception, CoppeliaSim for arms, and PyBullet or MuJoCo for reinforcement learning.
Pick one, get a robot running in it this week, and start breaking things safely. Watch the full rundown in the video above, and tell me which simulator you want a deeper walkthrough of next.
FAQ
What is the best robot simulator for ROS 2?
For most ROS 2 work, Gazebo is the default choice because of its native integration with the ROS 2 navigation and control stack. Webots is the easiest to start with, and Isaac Sim is preferred for AI and perception tasks that need photorealism.
Which robot simulator is best for beginners?
Webots and PyBullet are the most beginner-friendly. Webots is open source, cross-platform, and quick to set up with built-in robot models, while PyBullet is lightweight and Python-friendly, which makes it easy to start with for learning and reinforcement learning experiments.
Which simulator is best for reinforcement learning?
PyBullet and MuJoCo are the most popular for reinforcement learning. PyBullet is lightweight and easy to configure, and MuJoCo is fast and accurate enough to be the standard in research on manipulation, humanoids, and legged locomotion.
Is Gazebo or Isaac Sim better?
They serve different goals. Gazebo is better for general ROS 2 development and runs on modest hardware, while Isaac Sim is better for AI, perception, and synthetic data generation, but it requires a strong NVIDIA GPU and has a steeper learning curve.
Does Drift work with these simulators?
Drift generates simulation workspaces for ROS 2 across Gazebo, MuJoCo, and Isaac Sim (Isaac Sim coming soon) from natural-language prompts, including the robot description, world files, controllers, and plugin configuration.
