TrackerLab is a cutting-edge modular framework for humanoid motion retargeting, trajectory tracking, and skill-level control, built on top of IsaacLab.
Whether you're working with SMPL/FBX motion data, designing low-level whole-body controllers, or building skill graphs for high-level motion planning — TrackerLab brings everything together with a clean, extensible manager-based design.
Built to track, compose, and control humanoid motions — seamlessly from dataset to deployment.
| G1 Debug | G1 Running | G1 Jump |
|---|---|---|
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🧠 IsaacLab-Integrated Motion Tracking Seamlessly plugs motion tracking into IsaacLab's simulation and control framework using manager-based abstraction.
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🔁 Full Motion Retargeting Pipeline Converts SMPL/AMASS/FBX human motions into robot-specific trajectories with support for T-pose alignment, filtering, and interpolation.
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🎮 Versatile Command Control Modes Switch between multiple control paradigms like ex-body pose control, PHC, and more—using the powerful CommandManager.
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🔀 Skill Graph via FSM Composition Design complex motion behaviors using FSM-based skill graphs; supports manual triggers, planners, or joystick interfaces.
🎓 Want to understand TrackerLab quickly? 👉 Check out our full Tutorial (EN) or 教程 (中文版)
TrackerLab extends IsaacLab. Make sure IsaacLab and its dependencies are installed properly. Follow the official IsaacLab setup guide if needed.
# Clone TrackerLab
git clone https://github.com/interval-package/trackerlab.git
cd trackerlab
# Activate IsaacLab conda environment
conda activate <env_isaaclab>
# Install TrackerLab and poselib
pip install -e .
pip install -e ./poselib💡 No extra packages or repos required — it's fully self-contained!
- Download motion datasets: AMASS or CMU FBX.
- Apply the retargeting process (see tutorial).
- Organize data under
./data/as shown in data README.
- ✨ Fully modular and extensible
- 🤖 Designed for real-world humanoid control (e.g., Unitree H1)
- 📚 Clean codebase and manager-based environment design
- 🛠️ Easy integration of new motion sets and control modes
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📁 Project Structure Understand TrackerLab’s layout and modular system.
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🔄 Data Flow Learn how data flows through the tracking, retargeting, and control pipeline.
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🔄 Problems Problems you may encounter is recorded.
New training and testing tasks are registered under:
trackerLab/tasks/
Custom Gym environments are recursively registered, including H1TrackAll, and can be used directly with IsaacLab's training scripts.
Just add following lines into your train script:
import trackerLab.tasksWe also provide a copy from the orginal repo, for which you could directly run:
python scripts/rsl_rl/base/train.py --task H1TrackingWalk --headless
# H1 tasks that do not require generate usd, sine we use the isaaclab's usd, however it ruins the performance.For play just directly play it like:
# on gui
python scripts/rsl_rl/base/play.py --task <Your task> --num_envs 32 # In your work space dir
# on saved video
python scripts/rsl_rl/base/play.py --task <Your task> --num_envs 32 --headless --video --video_length 500 # In your work space dir
If you find TrackerLab helpful for your work or research, please consider citing:
@software{zheng2025@trackerLab,
author = {Ziang Zheng},
title = {TrackerLab: One step unify IsaacLab with multi-mode whole-body control.},
url = {https://github.com/interval-package/trackerLab},
year = {2025}
}Zaterval 📧 [email protected]
Looking for collaborators and contributors — feel free to reach out or open an issue!
This project is licensed under the MIT License. See LICENSE for details.