---
title: ABot-PhysWorld_SFT_Training_Data_v1
canonical_url: "https://www.modelscope.ai/models/amap_cvlab/ABot-PhysWorld_SFT_Training_Data_v1"
md_url: "https://www.modelscope.ai/models/amap_cvlab/ABot-PhysWorld_SFT_Training_Data_v1.md"
repository: amap_cvlab/ABot-PhysWorld_SFT_Training_Data_v1
last_updated: 2026-03-27
library_name:
  - pytorch
frameworks:
  - pytorch
downloads: 171
stars: 0
---

# ABot-PhysWorld_SFT_Training_Data_v1

> ABot-PhysWorld_SFT_Training_Data_v1 - An open-source model by amap_cvlab on ModelScope. 🤖 ABot-PhysWorld SFT Training Data (v1)

- **Repository**: amap_cvlab/ABot-PhysWorld_SFT_Training_Data_v1
- **Downloads**: 171
- **Stars**: 0
- **Last updated**: 2026-03-27

Source: https://www.modelscope.ai/models/amap_cvlab/ABot-PhysWorld_SFT_Training_Data_v1

---

<div align="center">

<h1>🤖 ABot-PhysWorld SFT Training Data (v1)</h1>

<p align="center">
  <b>AMAP CV Lab</b>
</p>

<p align="center">
  <a href="https://arxiv.org/abs/2603.23376"><img src="https://img.shields.io/static/v1?label=Paper&message=arXiv&color=red&logo=arxiv"></a>
  <a href="https://github.com/amap-cvlab/ABot-PhysWorld"><img src="https://img.shields.io/badge/Code-GitHub-blue?logo=github"></a>
  <a href="https://modelscope.cn/models/amap_cvlab/Abot-PhysWorld"><img src="https://img.shields.io/static/v1?label=Model&message=ModelScope&color=purple"></a>
</p>

</div>

> **Supervised Fine-Tuning (SFT) data** for [ABot-PhysWorld](https://github.com/amap-cvlab/ABot-PhysWorld) — a physically consistent, action-controllable video world model for robotic manipulation built on a 14B Diffusion Transformer.

## 📊 Dataset Overview

This is the **v1** release. The dataset contains **287,557** curated robotic manipulation video clips paired with dense textual descriptions, aggregated from five real-world sources.

| Source | Samples | Description |
|--------|---------|-------------|
| **AgiBot** | 125,259 | Agile robotic manipulation challenges |
| **OXE** | 77,110 | Open X-Embodiment cross-robot data |
| **RoboMIND** | 32,792 | Multi-robot manipulation with diverse end-effectors |
| **RoboCOIN** | 26,450 | Multi-embodiment collaborative manipulation |
| **Galaxea** | 25,946 | Open-world robotic interaction |
| **Total** | **287,557** | |

## 📁 Structure

```
.
├── ABot-PhysWorld_v1.jsonl      # Annotations: video path + text prompt
└── videos/
    ├── AgiBot/
    ├── OXE/
    ├── RoboMIND/
    ├── RoboCOIN/
    └── Galaxea/
```

## 📝 Data Format

Each line in `ABot-PhysWorld_v1.jsonl` is a JSON object:

```json
{
  "video": "videos/RoboMIND/.../trajectory_camera_left.mp4",
  "prompt": "The video opens with a view of a clean, well-lit industrial workspace..."
}
```

- **`video`**: Relative path to the MP4 video file
- **`prompt`**: Dense, multi-paragraph description covering initial scene setup, step-by-step action progression, final state, and camera perspective

## 🚀 Usage

```python
import json, os

DATA_ROOT = "/path/to/this/dataset"

with open(os.path.join(DATA_ROOT, "ABot-PhysWorld_v1.jsonl")) as f:
    for line in f:
        record = json.loads(line)
        video_path = os.path.join(DATA_ROOT, record["video"])
        prompt = record["prompt"]
```

## 📜 Citation

```bibtex
@article{abot-physworld2026,
  title={ABot-PhysWorld: Interactive World Foundation Model for Robotic Manipulation with Physics Alignment},
  author={Yuzhi Chen, Ronghan Chen, Dongjie Huo, Yandan Yang, Dekang Qi, Haoyun Liu, Tong Lin, Shuang Zeng, Junjin Xiao, Xinyuan Chang, Feng Xiong, Xing Wei, Zhiheng Ma, Mu Xu},
  year={2026}
}
```

## License

Released for **research purposes only**. Please refer to the original source datasets for their respective licenses.

## 🙏 Acknowledgement

- [RoboMIND](https://huggingface.co/datasets/x-humanoid-robomind/RoboMIND)
- [RoboCOIN](https://huggingface.co/RoboCOIN)
- [AgiBotWorld](https://huggingface.co/datasets/agibot-world/AgiBotWorld-Beta)
- [Galaxea](https://huggingface.co/datasets/OpenGalaxea/Galaxea-Open-World-Dataset)
- [Open X-Embodiment](https://github.com/google-deepmind/open_x_embodiment)