MuJoCo Parameter Identification System
Physics simulation and optimization system for material property identification through computational modeling
Overview
Developed comprehensive parameter identification framework for MuJoCo physics simulations, implementing SciPy optimization algorithms to estimate material properties (stiffness/damping) through integration of experimental video analysis with computational modeling.
Technologies Used
Key Highlights
- Implemented SciPy optimization for material property estimation
- Developed automated CI/CD pipeline with GitHub Actions
- Integrated experimental video analysis with physics simulation
- Applied mean square error minimization for parameter fitting
Introduction
This project focuses on developing advanced parameter identification techniques for MuJoCo physics simulations, specifically targeting material property estimation through computational modeling. The system bridges experimental data collection with simulation-based optimization to identify unknown system parameters like stiffness and damping coefficients.
Skills Used
- Physics Simulation: Advanced MuJoCo modeling and computational physics
- Optimization: SciPy-based parameter estimation and convergence algorithms
- Experimental Methods: Video analysis and data extraction techniques
- Software Engineering: CI/CD implementation with automated testing pipelines
- Data Analysis: Statistical modeling and error minimization approaches
Project
The parameter identification system addresses critical challenges in computational physics by creating an automated framework for estimating material properties from observed behavior. The system combines experimental data collection through Tracker video analysis with sophisticated MuJoCo physics simulations to determine unknown parameters.
SciPy optimization algorithms minimize mean square error between experimental observations and simulated predictions, enabling accurate identification of material properties including stiffness coefficients and damping parameters. The framework implements robust numerical methods to handle parameter sensitivity and avoid local minima during optimization.
The automated pipeline includes comprehensive validation testing and GitHub Actions CI/CD integration, ensuring reproducible results across different computational environments. This approach significantly reduces manual parameter tuning efforts while improving simulation accuracy for complex physical systems, establishing a foundation for automated calibration of physics-based models in robotics and material science applications.