MuJoCo
Google DeepMind's rigid-body physics engine for robotics. Contact-rich dynamics, MJCF models, plus MJX and MuJoCo Warp for batched GPU rollouts during policy training.
Overview
MuJoCo began as Emo Todorov's engine at the University of Washington, went commercial, then Google DeepMind bought it and open-sourced the whole thing under Apache 2.0 in 2022. The design bet is a soft-constraint contact solver that holds up at large timesteps, which is why it became the default for reinforcement learning on legged robots and dexterous hands. Models are written in MJCF, an XML format, and URDF import works with caveats around inertia and joint limits.
Three runtimes now share the same physics. Plain MuJoCo is C with Python bindings and runs happily on a laptop. MJX reimplements the core in JAX so you can batch thousands of environments across GPU or TPU. MuJoCo Warp, built with NVIDIA on the Warp framework, targets NVIDIA hardware specifically and reports over 100x gains against MJX on manipulation scenes, and once it leaves beta it folds back into MJX so existing MJX users inherit the speed. MuJoCo Menagerie ships vetted models for common hardware, which matters because hand-tuning MJCF is where most projects quietly lose a month.
The gap is perception. Rendering exists but is not photoreal, so teams doing camera-in-the-loop policy training pair MuJoCo with an external renderer or move to a USD pipeline. Release cadence is fast and breaking: 3.10 in June 2026, 3.11 in July, 3.12 in August, each carrying API or ABI changes. Pin your version and read the migration notes before upgrading a training cluster.
Key Features
- ✓ Soft-constraint contact solver that stays stable at large timesteps, which is why it became the default for legged locomotion and dexterous manipulation
- ✓ MJCF authoring plus URDF import, with MuJoCo Menagerie supplying tuned models for Franka arms, Shadow Hands, quadrupeds and humanoids
- ✓ MJX, a reimplementation of the core in JAX, for thousands of parallel environments across GPU or TPU
- ✓ MuJoCo Warp, built with NVIDIA on the Warp framework, reported over 100x faster than MJX on manipulation scenes and still in beta
- ✓ C API with Python bindings, an interactive viewer and passive-mode stepping for embedding in a training loop
- ✓ Version 3.12.0 on 20 August 2026 added a PID actuator with setpoint rate limiting and a rewritten box-box collision routine
Where it holds
- • The contact model is the reference other engines get measured against for sim-to-real on legged robots
- • Apache 2.0 top to bottom, no seat counts and no cloud dependency, so reproducing a result years later is realistic
- • Menagerie removes weeks of MJCF inertia and friction tuning for common hardware
- • Monthly releases with a changelog that states breaking changes plainly instead of hiding them
Where it breaks
- • Rendering is functional rather than photorealistic, so camera-in-the-loop perception training needs an external renderer or a move to a USD-based stack
- • MJX and MuJoCo Warp cover a subset of full MuJoCo features, so a scene that runs on CPU may not compile to the GPU path without edits
- • Versions 3.10, 3.11 and 3.12 all shipped breaking API or ABI changes inside three months, which is rough on pinned toolchains
- • Sensor and middleware simulation is thin next to Gazebo, so it is a dynamics engine, not a full robot software stack testbed
My Take
Ask anyone training locomotion policies which engine they trust for contact and the answer comes back the same. Release 3.12.0 on 20 August 2026 is the current reference point, and MuJoCo Warp already runs manipulation scenes over a hundred times faster than the JAX path, though it is beta and feature parity is incomplete. Where it loses is vision: the built-in renderer will not fool a perception model, and Isaac Sim wins that ground outright. For dynamics you can read, patch and reproduce, nothing else in the category is close.
Quick Info
- Pricing:
- open-source
- Openness:
- Open source
- Licence:
- Apache 2.0
- Starting at:
- Free. Apache 2.0 across MuJoCo, MJX and MuJoCo Warp, with no paid tier, licence server or hosted service. `pip install mujoco` pulls prebuilt wheels for Linux, macOS and Windows. The only cost is the hardware you run rollouts on, and the GPU paths add nothing to that bill.
- Added:
- Aug 2026
- Updated:
- Aug 2026
Use Cases
Judge it on your own work
The notes above say where MuJoCo holds and where it breaks. The fastest check is your own workload.
Visit website ↗Alternatives to MuJoCo
Genesis World
open-sourceOpen-source multi-physics simulator for robot learning. Rigid, FEM, MPM, SPH and PBD solvers share one Python scene, with a photoreal renderer and heavy GPU batching.
NVIDIA Isaac Sim
freeGPU-accelerated robotics simulator for physics-accurate testing, synthetic data generation and large-scale policy training, with ROS 2 bridging and sim-to-real workflows.