Upload GEAR-SONIC model and documentation

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+Version Release Date: October 24, 2025

README.md

@@ -1,7 +1,246 @@
 ---
 license: other
 license_name: nvidia-open-model-license
-license_link: >-
-  https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
-pipeline_tag: robotics
----
+license_link: LICENSE
+tags:
+- robotics
+- humanoid
+- whole-body-control
+- reinforcement-learning
+- motion-tracking
+- teleoperation
+- pytorch
+- isaac-lab
+pipeline_tag: reinforcement-learning
+---
+
+# GEAR-SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control
+
+<div align="center">
+  <img src="https://raw.githubusercontent.com/ZhengyiLuo/GR00T-WholeBodyControl/main/docs/source/_static/sonic-preview-gif-480P.gif" width="800">
+</div>
+
+## Model Description
+
+**SONIC** (Supersizing Motion Tracking) is a humanoid behavior foundation model developed by NVIDIA that gives robots a core set of motor skills learned from large-scale human motion data. Rather than building separate controllers for predefined motions, SONIC uses motion tracking as a scalable training task, enabling a single unified policy to produce natural, whole-body movement and support a wide range of behaviors.
+
+### Key Features
+
+- 🤖 **Unified Whole-Body Control**: Single policy handles walking, running, crawling, jumping, manipulation, and more
+- 🎯 **Motion Tracking Foundation**: Trained on large-scale human motion data for natural movements
+- 🎮 **Real-Time Teleoperation**: VR-based whole-body teleoperation via PICO headset
+- 🚀 **Hardware Deployment**: C++ inference stack for real-time control on humanoid robots
+- 🎨 **Kinematic Planner**: Real-time locomotion generation with multiple movement styles
+- 🔄 **Multi-Modal Control**: Supports keyboard, gamepad, VR, and high-level planning
+
+### Supported Behaviors
+
+**Locomotion:**
+- Walking (forward, backward, sideways)
+- Running
+- Kneeling
+- Hand crawling
+- Elbow crawling
+- Getting up from lying position
+- Jumping
+
+**Manipulation:**
+- Bimanual object manipulation
+- Object hand-off between hands
+- Coordinated arm-body movements
+
+**Stylized Motion:**
+- Happy walking
+- Stealth walking
+- Injured walking
+- Boxing stance
+
+## Quick Start
+
+### Installation
+
+```bash
+git clone https://github.com/ZhengyiLuo/GR00T-WholeBodyControl.git
+cd GR00T-WholeBodyControl
+git lfs pull
+```
+
+For detailed installation instructions, see the [Installation Guide](https://zhengyiluo.github.io/GR00T-WholeBodyControl/getting_started/installation_deploy.html).
+
+### Running SONIC
+
+```bash
+# Navigate to deployment directory
+cd gear_sonic_deploy
+
+# Run with keyboard control
+python scripts/deploy_sonic.py --control_mode keyboard
+
+# Run with gamepad control
+python scripts/deploy_sonic.py --control_mode gamepad
+
+# Run with kinematic planner
+python scripts/deploy_sonic.py --use_planner
+```
+
+## Model Checkpoints
+
+Pre-trained SONIC policy checkpoints are included in the repository under `gear_sonic_deploy/models/`. The models are optimized for:
+- Real-time inference on NVIDIA Jetson platforms
+- Low-latency control (< 50ms)
+- Robust sim-to-real transfer
+
+## Architecture
+
+SONIC uses a transformer-based policy architecture trained with:
+- **Training Framework**: Isaac Lab 2.3.0
+- **Backend**: PyTorch
+- **Deployment**: C++ inference with TorchScript
+- **Control Frequency**: 20-50 Hz
+- **Observation Space**: Proprioceptive sensing + motion tracking targets
+- **Action Space**: Joint position/velocity targets
+
+## Training Data
+
+SONIC is trained on large-scale human motion capture datasets including:
+- Locomotion sequences (walking, running, jumping)
+- Full-body manipulation tasks
+- Multi-modal movement styles
+- Ground-up recovery behaviors
+
+The model learns through motion tracking, which provides a scalable supervisory signal for acquiring diverse motor skills.
+
+## VR Teleoperation
+
+SONIC supports real-time whole-body teleoperation for data collection and interactive control:
+
+- **Hardware**: PICO VR headset + controllers
+- **Tracking**: Full-body motion retargeting
+- **Latency**: < 100ms end-to-end
+- **Applications**: Demonstration collection, direct control, behavior annotation
+
+See the [VR Teleoperation Guide](https://zhengyiluo.github.io/GR00T-WholeBodyControl/tutorials/vr_wholebody_teleop.html) for setup instructions.
+
+## Intended Use
+
+### Primary Use Cases
+
+✅ **Research & Development**
+- Humanoid robot control research
+- Whole-body motion learning
+- Sim-to-real transfer studies
+- Teleoperation system development
+
+✅ **Robotics Applications**
+- Mobile manipulation tasks
+- Human-robot interaction
+- Demonstration collection
+- Behavior cloning baselines
+
+### Out-of-Scope Use
+
+❌ This model is **NOT** intended for:
+- Safety-critical applications without additional validation
+- Autonomous operation in uncontrolled environments
+- Direct deployment without proper safety measures
+- Applications requiring certified safety guarantees
+
+## Limitations
+
+- **Hardware Specific**: Models are tuned for specific humanoid platforms (primarily Unitree G1)
+- **Sim-to-Real Gap**: Performance may vary across different hardware configurations
+- **Safety**: Requires proper safety protocols during deployment
+- **Generalization**: Best performance on behaviors similar to training distribution
+- **Environment**: Designed for structured indoor environments
+
+## Hardware Requirements
+
+### Deployment Platform
+- **Recommended**: NVIDIA Jetson Orin (32GB or 64GB)
+- **Minimum**: NVIDIA GPU with CUDA support
+- **Robot**: Compatible humanoid platforms (Unitree G1, H1, etc.)
+
+### Development Platform
+- **GPU**: NVIDIA GPU with 16GB+ VRAM (for training)
+- **RAM**: 32GB+ recommended
+- **Storage**: 100GB+ for datasets and checkpoints
+
+## Documentation
+
+📚 **[Full Documentation](https://zhengyiluo.github.io/GR00T-WholeBodyControl/)**
+
+### Guides
+- [Installation (Deployment)](https://zhengyiluo.github.io/GR00T-WholeBodyControl/getting_started/installation_deploy.html)
+- [Installation (Training)](https://zhengyiluo.github.io/GR00T-WholeBodyControl/getting_started/installation_training.html)
+- [Quick Start](https://zhengyiluo.github.io/GR00T-WholeBodyControl/getting_started/quickstart.html)
+- [VR Teleoperation Setup](https://zhengyiluo.github.io/GR00T-WholeBodyControl/getting_started/vr_teleop_setup.html)
+
+### Tutorials
+- [Keyboard Control](https://zhengyiluo.github.io/GR00T-WholeBodyControl/tutorials/keyboard.html)
+- [Gamepad Control](https://zhengyiluo.github.io/GR00T-WholeBodyControl/tutorials/gamepad.html)
+- [VR Whole-Body Teleoperation](https://zhengyiluo.github.io/GR00T-WholeBodyControl/tutorials/vr_wholebody_teleop.html)
+
+## Repository Structure
+
+```
+GR00T-WholeBodyControl/
+├── gear_sonic_deploy/     # C++ inference stack for deployment
+├── gear_sonic/            # Teleoperation and data collection tools
+├── decoupled_wbc/         # Decoupled WBC (GR00T N1.5/N1.6)
+├── docs/                  # Documentation source
+└── media/                 # Videos and images
+```
+
+## Related Projects
+
+This repository is part of NVIDIA's GR00T (Generalist Robot 00 Technology) initiative:
+- **[GR00T N1.5](https://research.nvidia.com/labs/gear/gr00t-n1_5/)**: Previous generation decoupled controller
+- **[GR00T N1.6](https://research.nvidia.com/labs/gear/gr00t-n1_6/)**: Improved decoupled WBC approach
+- **[GEAR-SONIC Website](https://nvlabs.github.io/GEAR-SONIC/)**: Project page with videos and details
+
+## Citation
+
+If you use GEAR-SONIC in your research, please cite:
+
+```bibtex
+@article{luo2025sonic,
+    title={SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control},
+    author={Luo, Zhengyi and Yuan, Ye and Wang, Tingwu and Li, Chenran and Chen, Sirui and Casta\~neda, Fernando and Cao, Zi-Ang and Li, Jiefeng and Minor, David and Ben, Qingwei and Da, Xingye and Ding, Runyu and Hogg, Cyrus and Song, Lina and Lim, Edy and Jeong, Eugene and He, Tairan and Xue, Haoru and Xiao, Wenli and Wang, Zi and Yuen, Simon and Kautz, Jan and Chang, Yan and Iqbal, Umar and Fan, Linxi and Zhu, Yuke},
+    journal={arXiv preprint arXiv:2511.07820},
+    year={2025}
+}
+```
+
+## License
+
+This project uses **dual licensing**:
+
+- **Source Code**: Apache License 2.0 - applies to all code, scripts, and software components
+- **Model Weights**: NVIDIA Open Model License - applies to all trained model checkpoints
+
+**Key points of the NVIDIA Open Model License:**
+- ✅ Commercial use permitted with attribution
+- ✅ Modification and distribution allowed
+- ⚠️ Must comply with NVIDIA's Trustworthy AI terms
+- ⚠️ Model outputs subject to responsible use guidelines
+
+See [LICENSE](https://github.com/ZhengyiLuo/GR00T-WholeBodyControl/blob/main/LICENSE) for complete terms.
+
+## Support & Contact
+
+- 📧 **Email**: [gear-wbc@nvidia.com](mailto:gear-wbc@nvidia.com)
+- 🐛 **Issues**: [GitHub Issues](https://github.com/ZhengyiLuo/GR00T-WholeBodyControl/issues)
+- 📖 **Documentation**: [https://zhengyiluo.github.io/GR00T-WholeBodyControl/](https://zhengyiluo.github.io/GR00T-WholeBodyControl/)
+- 🌐 **Website**: [https://nvlabs.github.io/GEAR-SONIC/](https://nvlabs.github.io/GEAR-SONIC/)
+
+## Acknowledgments
+
+This work builds upon and acknowledges:
+- [Beyond Mimic](https://github.com/HybridRobotics/whole_body_tracking) - Whole-body tracking foundation
+- [Isaac Lab](https://github.com/isaac-sim/IsaacLab) - Robot learning framework
+- NVIDIA Research GEAR Lab team
+- All contributors and collaborators
+
+## Model Card Contact
+
+For questions about this model card or responsible AI considerations, contact: [gear-wbc@nvidia.com](mailto:gear-wbc@nvidia.com)