mujoco-mojo 1.7.4

A complete MJCF lifecycle and trial orchestration suite for MuJoCo, powered by Pydantic v2.

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A complete MJCF lifecycle and trial orchestration suite for MuJoCo, powered by Pydantic v2.

MuJoCo Mojo bridges the gap between static XML modeling and large-scale simulation research. It provides a strongly-typed bridge for building models and a robust execution engine for running them at scale.

• Model: Build MJCFs via validated Python objects allowing for programatic generation.
• Scale: Execute multi-threaded Monte Carlo trials with built-in resume logic.
• Monitor: Track progress via a zero-dependency web dashboard and persistent logs.
• Assess: Quickly view interactive results of a trial in context of others.
• Reproduce: Automatic environment snapshotting (requirements.txt) for every job.

Installation

Install using uv (recommended):

uv add mujoco-mojo

or with pip:

pip install mujoco-mojo

[!WARNING] At the time of writing, MuJoCo supports up to Python 3.13

Features

MJCF Tools

• Strongly-typed MJCF elements backed by Pydantic v2
• Early validation of MJCF structure and attribute semantics
• Pythonic composition of assets, bodies, sensors, and plugins
• Designed to mirror MuJoCo’s XML schema closely (no magic abstractions)
• Suitable for code generation, tooling, and large model pipelines
• Embedded MuJoCo object enumerations to make getting mjOBJ IDs simple
• Specialized handling of dependency by remapping assets to become shared allows for space efficient execution of complex models

Job Utilities

• Single or multi-threaded trial execution
• Random draw tools for Monte Carlo or rerun with global variable override
• Detailed status files for insight on trial progress
• Resume a previously started job without rerunning previous cases
• Automatically record installed Python packages to requirements.txt for job recreation (works with uv or pip)
• End of run summary with metric to help perform a state of health check
• Flexible command line utilities to run jobs

[!TIP]

mujoco-mojo run monte-carlo \
    --generator monte_carlo_test.Experiment.generate \
    --runtime monte_carlo_test.runtime \
    --n-trial 10 \
    --n-proc 4

• Support for running jobs with SLURM for distributed compute
• Built in Rich logging for terminal and a rotating file handler for persistent logs

Dojo Dashboard

A zero-dependency, offline-first web suite for monitoring and analyzing your simulation jobs in real-time.

Monitor: Real-Time Oversight

• Live Progress Tracking: Dynamic progress bars and color-coded status cards provide a high-level view of your Monte Carlo runs.
• Success/Failure Analytics: Automatic categorization of trials with built-in data integrity checks to identify "empty" vs. "failed" runs.
• Sensory Feedback: Optional audio cues and visual celebrations let you know exactly when a multi-hour job hits 100%.
• Deep-Linked Navigation: Jump straight from the monitor to any individual trial in the viewer with one click.

Mosaic: Advanced Telemetry Analysis

• High-Fidelity Plotting: Hardware-accelerated visualization using Plotly.js for seamless zooming and panning through millions of data points.
• Dynamic Versus Mode: Overlay current telemetry against previous trials using an intuitive range-selection slider for instant regression testing.
• Regex-Powered Filtering: Navigate high-dimensional datasets using a "folder-style" signal selector with suffix and regex support.
• State Persistence & Sharing: Every view is captured in a shareable, compressed URL by pasting a link to share your exact configuration.
• Pro-Grade Tooling: Built-in JSON configuration editor, drag-and-drop config restoration, and multi-format exports (SVG, PNG, CSV).
• Keyboard-First Design: Full hotkey support for warping between trials and managing views without leaving the home row.

Reloaded

A rapid prototyping loop that allows you to modify physics logic and model architecture on the fly without ever closing the visualizer.

• Module Hot-Reloading: Recursively reloads local Python modules and MJCF logic, allowing code changes to propagate instantly to the active simulation.
• Unified Visualizer Bridge: Synchronized visualization of custom force and torque vectors across native OpenGL, Viser web interfaces, and video recordings.
• Interactive Prototyping: A developer-centric command loop to toggle playback speeds, repeat last commands, or trigger "generation-only" mode for rapid MJCF debugging.
• Asset Persistence: Automatically dumps current MJCF snapshots and model configurations to a workspace directory for post-hoc analysis or version tracking.

[!TIP]

mujoco-mojo reloaded \
    --generator monte_carlo_test.Experiment.generate \
    --runtime monte_carlo_test.runtime \

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[!NOTE] MuJoCo Mojo is an independently developed open-source toolbox. It is not affiliated with, sponsored by, or endorsed by Google DeepMind or the official MuJoCo development team. MuJoCo® is a registered trademark of Google LLC. All MJCF schemas and MuJoCo-related terminology used within this project are for compatibility and documentation purposes only.