tune-mjcf

Tune MuJoCo MJCF simulation parameters to balance speed and physics accuracy.

Updated Feb 24, 2026

Search Tags:#optimization #simulation #performance tuning #mjcf #mujoco #physics accuracy

Ready to run

npx skills add https://github.com/Zurybr/lefarma-skills --skill tune-mjcf

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: tune-mjcf
Source: https://github.com/Zurybr/lefarma-skills/tree/main/letta/benchmarks/trajectory-only/tune-mjcf
Command: npx skills add https://github.com/Zurybr/lefarma-skills --skill tune-mjcf

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps users optimize MuJoCo MJCF simulation files to improve performance and reduce computation time while maintaining essential physics accuracy.

Core Features & Use Cases

Performance Tuning: Adjust simulation parameters like timestep, solver iterations, and flags to speed up simulations.
Accuracy vs. Speed Trade-offs: Systematically explore parameters to find the optimal balance between simulation speed and physical fidelity.
Use Case: When a MuJoCo simulation runs too slowly for real-time applications or rapid iteration, this skill guides you through tuning the MJCF file to achieve acceptable performance without breaking the physics.

Quick Start

Use the tune-mjcf skill to optimize the attached simulation file 'robot.mjcf' for faster execution.

Frequently Asked Questions about tune-mjcf

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I optimize MuJoCo MJCF files for faster simulation execution?▼

To optimize MuJoCo MJCF files, you adjust simulation parameters like timestep, solver iterations, and flags to reduce computation time. This process balances simulation speed against physical fidelity to ensure real-time performance.

What is the best way to balance physics accuracy and speed in MuJoCo?▼

Balancing physics accuracy and speed in MuJoCo requires systematic parameter sweeps of your MJCF file. This approach systematically explores simulation parameters to find the optimal trade-off between rapid execution and physical fidelity.

How do I reduce computation time in a MuJoCo physics simulation?▼

Reducing computation time in a MuJoCo physics simulation involves adjusting MJCF parameters like timestep and solver iterations. This performance tuning directly targets constraint solver errors to speed up iterations without breaking the physics.

Do I need to understand integration discretization error to tune MuJoCo models?▼

Yes, tuning MuJoCo models requires an understanding of integration discretization error and constraint solver error. This knowledge is necessary to systematically adjust parameters and achieve acceptable performance without compromising physics accuracy.

Why does my MuJoCo simulation run too slowly for real-time applications?▼

MuJoCo simulations run too slowly when MJCF parameters like timestep and solver iterations are not optimized for the specific model. Adjusting these flags and parameters reduces computation time to achieve acceptable real-time performance.