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MuJoCo for simulating robot dynamics
Model articulated systems, contacts and actuators in a physics simulation.
Key Takeaways
- MuJoCo uses model descriptions to represent bodies, joints and physical properties.
- Start from a known model definition and expected actuator ranges.
- Load a simple articulated model and test known actuator commands.
- A simulation only reflects its specified parameters.
How it works
MuJoCo uses model descriptions to represent bodies, joints and physical properties. Its API advances simulation and exposes state for controllers, learning or analysis.
What you need
Start from a known model definition and expected actuator ranges. Keep physics parameters and controller update rates explicit so the same movement can be repeated.
What to try first
Load a simple articulated model and test known actuator commands. Change one physical parameter and compare the resulting trajectory.
Compare two simulation timesteps on a known movement. Time integration and controller updates can affect stability even when the model geometry is unchanged.
Where to look for failures
A simulation only reflects its specified parameters. Check units, timing and contact behavior before using results to predict real-world motion.
Related Franklin pages
Separate the physics simulator, environment interface and learning algorithm. Test resets and termination before increasing the number of workers or training steps.
- Define and interact with environments through a shared reset and step interface. Gymnasium for reinforcement-learning environments
- Train and evaluate agents using established PyTorch implementations. Stable Baselines3 for standard reinforcement-learning algorithms
Franklin’s robotics coverage gives examples of where simulation, training and hardware meet. Franklin’s robotics coverage
Project documentation
Try it for your workflow
Explore the project’s documentation for setup and usage, or save this page to a collection for later.
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