MuJoCo vs Gazebo
These two get compared constantly and they are not really the same kind of tool. MuJoCo is a physics engine you build experiments on top of. Gazebo is a robot simulator that models the whole system, sensors included. That difference decides almost everything else.
Updated 2026-08-21
Choose MuJoCo if the hard part is physics: contact-rich manipulation, locomotion, and reinforcement learning where you need millions of fast, stable steps. Choose Gazebo if the hard part is the robot: ROS 2 integration, lidar and depth sensors, navigation stacks, and multi-robot scenes. Many teams use both — train the policy in MuJoCo, validate the system in Gazebo. Both are free and Apache 2.0.
Side-by-side comparison
| Dimension | MuJoCoFast contact-rich physics | GazeboROS-native robot simulation |
|---|---|---|
| What it is | A physics engine with a thin simulation layer around it | A full robot simulator: physics, sensors, plugins, and transport |
| Developer | Google DeepMind | Open Robotics |
| License | Apache 2.0 | Apache 2.0 |
| Physics engine | MuJoCo's own soft-constraint solver | Pluggable — ODE by default, also Bullet, DART, Simbody |
| Contact handling | Stable at large timesteps; the reason it dominates manipulation research | Workable, but contact-rich grasping is where teams hit its limits |
| Parallel simulation | MJX runs thousands of environments on GPU or TPU via JAX | One world per process; scale by running more processes |
| Sensor simulation | Basic — rangefinders, IMU, touch, force/torque | Mature — lidar, depth and RGB cameras, IMU, GPS, contact, with noise models |
| ROS / ROS 2 | Community wrappers, not native | Native, via ros_gz |
| Asset format | MJCF XML | SDF, and URDF via conversion |
| Rendering | Basic OpenGL, adequate for debugging | OGRE, built to drive simulated cameras |
| Hardware floor | CPU; GPU or TPU only if you use MJX | CPU |
| Strongest at | Dexterous manipulation, locomotion, RL iteration speed | Whole-robot systems, navigation, sensor-driven autonomy |
The comparison people actually mean
Search interest treats these as alternatives, but they answer different questions. MuJoCo (Google DeepMind, Apache 2.0) is a physics engine: you give it a model and it integrates dynamics extremely fast and extremely stably. It does not try to be your robot's software stack. Gazebo (Open Robotics) simulates the robot as a system — physics plus sensors plus plugins plus the message transport your ROS 2 nodes are already speaking. If you are asking "which simulator is better", the more useful question is which half of the problem you are currently stuck on.
Where MuJoCo wins: contact and iteration speed
MuJoCo's solver was designed for contact-rich dynamics, and it stays stable at timesteps that make other engines jitter or explode. That is why it became the default for dexterous manipulation and legged locomotion research. The second advantage is throughput: MJX reimplements the engine in JAX and runs thousands of environments in parallel on a GPU or TPU, which turns a multi-day RL run into a few hours. If your bottleneck is how many environment steps you can afford, this is the deciding factor and it is not close.
Where Gazebo wins: sensors and the rest of the robot
Gazebo models the things MuJoCo deliberately leaves out. Lidar returns, depth and RGB cameras, IMU drift, GPS, contact sensors — all with configurable noise, and all publishing on topics your existing ROS 2 nodes subscribe to without a translation layer. For navigation, SLAM, multi-robot coordination, or anything where perception and control are the system under test, that ecosystem is the product. Rebuilding it on top of MuJoCo is possible and is usually a mistake.
You are allowed to use both
The common pattern is not a choice at all: train the policy in MuJoCo where the steps are cheap, then validate the whole system in Gazebo where the sensors and the ROS 2 stack are real. The friction is not conceptual, it is format — the same robot and the same scene have to exist as MJCF for one and SDF for the other, with physics properties that agree. That duplication is where the time goes, and it is worth planning for before it becomes two diverging asset libraries.
When to choose each
Manipulation and locomotion research, reinforcement learning at scale, and any work where contact accuracy and steps-per-second decide the outcome.
ROS 2 robots, sensor-driven autonomy, navigation and SLAM, multi-robot scenes, and testing the full software stack rather than a policy.
Where Rigyd fits
If you run both — and most teams training policies eventually do — the asset problem doubles. The same object needs MJCF for MuJoCo and SDF or USD for Gazebo, with mass, friction, and collision geometry that actually match, or the policy you trained will not behave the same way on the other side. Rigyd generates native MJCF and native OpenUSD from one source asset, with the same derived physics in both, so moving between the two simulators is an export rather than a re-authoring job.