EquiMus: Musculoskeletal Equivalent Dynamic Modeling and Simulation for Rigid-soft Hybrid Robots with Linear Elastic Actuators

Table of Contents

• EquiMus: Musculoskeletal Equivalent Dynamic Modeling and Simulation for Rigid-soft Hybrid Robots with Linear Elastic Actuators
  • ✏️ What is EquiMus?
  • 💡 What Problems Does EquiMus Solve?
    • 1. Soft actuators are difficult to model—especially with dynamic mass redistribution
    • 2. Musculoskeletal systems contain kinematic loops
    • 3. High-fidelity soft-body simulation is too slow for control & RL
    • 4. No unified workflow from CAD → simulation → real robot
  • 🎶 Design Philosophy
  • 🚀 Install & Run EquiMus (3 minutes)
    • Optional: Download Validation Data
    • Optional: Install FFmpeg (for video logging)
  • 📂 Project Structure
  • 🧭 Roadmap
  • ✉️ Contact
  • 📖 Citation

✏️ What is EquiMus?

EquiMus is an energy-equivalent dynamic modeling framework that brings musculoskeletal robots into real-time simulation, based on MuJoCo. It converts soft actuators (e.g., pneumatic muscles, FEAs) into a rigorously derived 3-2-1 lumped-mass representation, preserving their true kinetic, potential, damping, and actuation energies.

This allows EquiMus to accurately model dynamic mass redistribution, biarticular routing, and kinematic loops within standard MuJoCo workflows—features that are traditionally hard (or impossible) to simulate.

EquiMus provides:

• A physically consistent actuator model validated in theory, simulation, and hardware
• A MuJoCo-compatible implementation supporting complex multi-muscle topologies
• Static, dynamic, and morphological validation results
• Downstream examples including PID auto-tuning, model-based control, and RL tasks

👉 In short: EquiMus makes muscle-driven robots truly simulatable.

‼️ Note: Our repository EquiMus focuses on the verification of the energy-equivalent modeling method in MuJoCo simulation at this stage. For real-world application, we are still working on building a complete open-source toolbox EquiMus-Toolbox for musculoskeletal robot design, fabrication, and sim-to-real deployment. Stay tuned for updates!

💡 What Problems Does EquiMus Solve?

EquiMus addresses four long-standing pain points in musculoskeletal robot simulation:

1. Soft actuators are difficult to model—especially with dynamic mass redistribution

Most simulators approximate muscles as massless springs, leading to incorrect dynamics.
→ EquiMus preserves actuator inertia, elasticity, damping, and work through an energy-equivalent formulation.

2. Musculoskeletal systems contain kinematic loops

URDF/PyBullet cannot represent multi-joint muscles or loop closures.
→ EquiMus uses MJCF with automatically generated constraints to model loops safely and robustly.

3. High-fidelity soft-body simulation is too slow for control & RL

FEM and Cosserat rod models are accurate but far from real time.
→ EquiMus achieves MuJoCo-level performance (>140× real-time) while retaining physical consistency.

4. No unified workflow from CAD → simulation → real robot

Current workflows require hand-built hacks and produce inconsistent dynamics.
→ EquiMus provides a clean, reproducible end-to-end pipeline, including calibration and sim-to-real examples.

🎶 Design Philosophy

[EN] EquiMus fakes how nature uses energy to control motion — through an energy-equivalent formulation that turns physics intuition into simulation reality. That is the “fake it until you make it” philosophy in the energy domain.

[CN] 假之以能量，得之于运动。以伪成真，以虚造实。自然之道，人之所驭。

🚀 Install & Run EquiMus (3 minutes)

1. Clone the repo & install dependencies

git clone https://github.com/fly-pigTH/EquiMus.git
cd EquiMus
conda create -n equimus python=3.11 -y
conda activate equimus
pip install -r requirements.txt

2. Quick check

python -c "import mujoco; print('MuJoCo:', mujoco.__version__)"

3. Run the demo (2-DOF & 3-DOF musculoskeletal morphologies)

# Display help message
python demo/demo.py -h

# macOS (mjpython)
mjpython demo/demo.py --dof 3

# Windows/Linux
python demo/demo.py --dof 3

If successful, a MuJoCo passive-viewer will open:

(a) 3-DOF Morphology	(b) 2-DOF Morphology

You can rotate, drag, and actuate the model using standard MuJoCo GUI controls following (MuJoCo interaction conventions).

Optional: Download Validation Data

Dynamic-validation examples require extra datasets:

👉 Download here: https://drive.google.com/file/d/1wKS-2Aa7IVpy4-AHKAF68sH8nZ6IhQTV/view?usp=drive_link

Place the extracted data/ folder inside src/validation_simulation/dynamic/. Static & morphology validation demos already include necessary data.

Optional: Install FFmpeg (for video logging)

• MacOS

brew install ffmpeg

• Windows

conda install -c conda-forge ffmpeg

• Linux

sudo apt update
sudo apt install ffmpeg

📂 Project Structure

The project is structured to mirror the workflow presented in the paper, making it easy to navigate and reproduce all experiments. Each module includes its own ReadMe for quick reference.

EquiMus/
├── models/                         # MuJoCo XML models
│   ├── 2DOF/
│   ├── 3DOF/
│   └── ...
│
├── src/
│   ├── validation_simulation/      # Simulation-based validation
│   │   ├── static/                 # ├─ Static validation
│   │   │   └── ReadMe.md
│   │   ├── dynamic/                # ├─ Dynamic validation
│   │   │   └── ReadMe.md
│   │   └── morphology/             # └─ Morphology generalization
│   │       └── ReadMe.md
│   │
│   ├── validation_physical/        # Real-world experiments
│   │   ├── static/
│   │   ├── dynamic/
│   │   └── ReadMe.md
│   │
│   ├── application/                # Downstream usage demos
│   │   ├── PID_AutoTuning/         # ├─ PID auto-tuning demo
│   │   │   └── ReadMe.md
│   │   └── RL_BallKicking/         # └─ RL ball-kicking demo
│   │       └── ReadMe.md
│   │
│   └── utils/
│       ├── experiment.py           # Utility script for experiments.
│       └── experiment_topology.py
│
├── demo/                           # Demo scripts & figures
│
├── media/                          # Logos & website resources
│
├── requirements.txt
├── LICENSE
└── ReadMe.md                       # Main documentation

🧭 Roadmap

We are actively developing EquiMus and have outlined the following roadmap for future enhancements and features:

• EquiMus Toolbox (will be released before December 2025)
  • Automated actuator parsing from URDF/MJCF
  • Automatic 3-2-1 reconstruction & constraint generation
  • Support for batch model conversion
• Model Library Expansion
  • Single-joint actuator examples (FEA, McKibben)
  • Biarticular / multi-joint muscles
  • Rigid–soft hybrid benchmark models
• Parameter Identification Suite
• Documentation & Website
  • Tutorials & interactive examples
  • Model Zoo on website

✉️ Contact

If you are interested in collaborating, have questions, or need support, please feel free to reach out to us!

• Yinglei Zhu: zylbhsf@gmail.com
• Huichan Zhao: zhaohuichan@mail.tsinghua.edu.cn

📖 Citation

If you find EquiMus helpful, please cite us:

@article{zhu2025equimus,
  title={EquiMus: Energy-Equivalent Dynamic Modeling and Simulation of Musculoskeletal Robots Driven by Linear Elastic Actuators},
  author={Zhu, Yinglei and Dong, Xuguang and Wang, Qiyao and Shao, Qi and Xie, Fugui and Liu, Xinjun and Zhao, Huichan},
  journal={IEEE Robotics and Automation Letters},
  year={2025},
  publisher={IEEE}
}