Ropedia / ropedia-w0

# Ropedia W0: Scaling Loco-Manipulation World Models across Embodiments with Human Experience
https://github.com/user-attachments/assets/5c870c18-7bab-4433-91df-1da36e50c90c
**Ropedia W0** is an egocentric world foundation model that learns **loco-manipulation** from **10k hours of human experience** and transfers it across embodiments, from robot arms to humanoids and from grippers to dexterous hands, through **one shared kinematic interface** of camera poses and projected skeletons.
## 🔥 Highlights
- **Model: One World Model, Multiple Embodiments.** A unified egocentric loco-manipulation world foundation model spanning humans and diverse robots, from robotic arms to humanoids with grippers or dexterous hands, conditioned on camera poses and projected kinematic skeletons.
- **Data: 13K Hours of Unified Experience.** 10K hours of human and 3K hours of robot egocentric video, with metric camera trajectories, kinematic skeletons, and hierarchical captions.
- **Scaling: Scaling Human Experience to Robots.** More human pre-training data consistently improves world prediction for both humans and robots.
- **Performance: State of the Art Across Embodiments.** Leading results on five robot benchmarks and held-out human videos, spanning zero-shot, few-shot, and full-data post-training settings.
- **Applications: From Prediction to Interaction and Planning.** Supports real-time, minute-long interactive VR rollouts through distillation, alongside policy evaluation, model-based planning, and world-action modeling.
## 📝 To-Do List
- [x] Release technical report
- [x] Release inference code (5B)
- [x] Release 5B checkpoints, pre-trained and mid-trained (Hugging Face)
- [x] Release Xp-Teleop teleoperation data (Hugging Face)
- [ ] Release A14B checkpoints and inference code
- [ ] Release benchmark fine-tuned checkpoints (5B and A14B on Xp-Teleop, ω-HOME, Ψ0 and GR-1)
- [ ] Release evaluation code and benchmark data
- [ ] Release post-training code
## 🛠️ Installation
### Prerequisites
- **System:** Linux.
- **Hardware:** an NVIDIA GPU with at least 40 GB of memory for the 5B model (31 GB peak at 640×480, 81 frames). The code has been verified on H200.
- **Software:**
 - Conda with Python 3.12.
 - An NVIDIA driver supporting CUDA 12.8. For CUDA 13 drivers see ENVIRONMENT.md.
 - No custom CUDA extensions are needed; attention uses PyTorch SDPA.
### Installation Steps
1. Clone the repo:
 [code] 
2. Create the environment and install the dependencies (torch 2.11 with CUDA 12.8):
 [code] 
3. Download the Wan2.2-TI2V-5B base model (the full repository):
 [code] 
 Any other location works with `--base_model_dir` (or `BASE_MODEL_DIR` for the scripts).
## 📦 Pretrained Weights
The Ropedia W0 checkpoints are hosted on Hugging Face at `ropedia-ai/Ropedia-W0`. Place them under `weights/` (or pass their paths to the scripts below).
| Model | Parameters | Training data | Resolution | File |
|---|---|---|---|---|
| Ropedia W0 5B (mid-trained) | 5B | human + robot video | 640×4