Datasets:

LeCAR-Lab
/

Wanda

---
license: cc-by-4.0
task_categories:
- robotics
tags:
- LeRobot
- robotics
- manipulation
- mobile-manipulation
- imitation-learning
- agibot-g1
- synthetic-data
pretty_name: WANDA — Worlds in One Demo
size_categories:
- 10K<n<100K
---

# WANDA: Worlds in One Demo

### A Synthetic Data Engine for Learning Open-World Mobile Manipulation

**🌐 Project page: https://wanda.lecar-lab.org/** · 📄 [Paper (PDF)](https://wanda.lecar-lab.org/assets/paper/WANDA.pdf) · 💻 Code (coming soon) · 🕹️ [Interactive 4D viewer](https://wanda.lecar-lab.org/)

Lingxiao Guo\*, Huanyu Li\*, Guanya Shi — **Carnegie Mellon University**
<sub>\*Equal contribution; order decided by a coin flip.</sub>

---

WANDA is a synthetic data engine that turns **one** human demonstration into diverse training data for
open-world mobile manipulation. From a single RGB-D demo, WANDA reconstructs the scene (Gaussian-splat
background, object meshes, and interaction trajectories), then re-generates it across new spatial
configurations, scenes, and even robot embodiments — producing the large-scale demonstration sets used
to train the vision-language-action (VLA) policies in the paper.

> *Robot data has been priced in human hours. WANDA prices it in compute.*

This repository hosts the WANDA-generated training datasets for the **five** long-horizon tasks
evaluated in the paper, each learned from a single real human demonstration on the **Agibot G1**.
Policies trained on this data reach 54.8% task progress on real-world rollouts and outperform policies
trained on 50 teleoperated demonstrations — while one hour of training data is generated in ≈3 GPU-hours.

## Dataset overview

| Subset | Task instruction | Episodes | Task chunks |
|---|---|---:|---|
| `drop_trash` | Pick up the empty can from the table and drop it into the trash bin. | 3,496 | task-0051 … 0054 |
| `fridge` | Pick up the snack bag, put it into the fridge, and close the door. | 436 | task-0056 |
| `lunch_box` | Close the lunch box on the table, pick it up, and throw it into the sink. | 2,043 | task-0052 … 0054 |
| `pour` | Pick up the kettle and pour water into the cups. | 7,775 | task-0055 … 0062 |
| `utensils` | Pick up the utensils from the table, place them in the basket, and put the basket in the sink. | 3,172 | task-0053 … 0056 |
| **Total** | | **16,922** | ~26.8M timesteps (≈248 h @ 30 FPS) |

Each subset is a **self-contained [LeRobot](https://github.com/huggingface/lerobot) v2.1 dataset**
(`meta/`, `data/`, `videos/`). Different task chunks within a subset correspond to different
spatial configurations / scenes generated by WANDA for the same task.

## Format

- **Robot:** `agibot_g1` (dual-arm mobile manipulator) · **Control/record rate:** 30 FPS · **Codebase:** LeRobot `v2.1`

**Per-frame features (parquet):**

| Key | Dtype | Shape | Description |
|---|---|---|---|
| `observation.state` | float32 | [20] | Proprioceptive robot state |
| `action` | float32 | [20] | Action target |
| `observation.cam_rel_poses` | float32 | [21] | Camera poses relative to the robot |
| `observation.task_info` | float32 | [46] | Task/goal conditioning vector |
| `observation.images.rgb.head` | video | [800, 1280, 3]† | Head camera |
| `observation.images.rgb.left_wrist` | video | [480, 848, 3] | Left-wrist camera |
| `observation.images.rgb.right_wrist` | video | [480, 848, 3] | Right-wrist camera |
| `timestamp`, `index`, `episode_index`, `task_index` | — | — | LeRobot bookkeeping |

<sub>† Head-camera resolution is 800×1280 for all subsets except `utensils` (480×768).</sub>

**Directory layout (per subset):**

```
<subset>/
├── meta/
│   ├── info.json            # features, fps, robot_type, totals
│   ├── tasks.jsonl          # natural-language task instructions
│   ├── episodes.jsonl       # per-episode index
│   └── episodes_stats.jsonl # per-episode feature statistics
├── data/
│   └── task-XXXX/episode_XXXXXXXX.parquet
└── videos/
    └── task-XXXX/observation.images.rgb.<cam>/episode_XXXXXXXX.mp4
```

## Usage

Download a single subset and load it with LeRobot:

```python
from huggingface_hub import snapshot_download
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset

root = snapshot_download(
    "LeCAR-Lab/Wanda",
    repo_type="dataset",
    allow_patterns="utensils/*",   # one of: drop_trash, fridge, lunch_box, pour, utensils
)
ds = LeRobotDataset(repo_id="LeCAR-Lab/Wanda", root=f"{root}/utensils")
print(ds)
print(ds[0].keys())
```

Or read the raw parquet directly (no LeRobot required):

```python
import pandas as pd
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    "LeCAR-Lab/Wanda",
    "utensils/data/task-0053/episode_00530010.parquet",
    repo_type="dataset",
)
df = pd.read_parquet(path)
print(df.shape, df.columns.tolist())
```

## License

Released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). If you use this dataset,
please provide attribution and cite the paper below.

## Citation

```bibtex
@article{guo2026wanda,
  title   = {Worlds in One Demo: A Synthetic Data Engine for Learning Open-World Mobile Manipulation},
  author  = {Guo, Lingxiao and Li, Huanyu and Shi, Guanya},
  year    = {2026}
}
```

## Links

- **Project page & interactive 4D viewer:** https://wanda.lecar-lab.org/
- **Paper (PDF):** https://wanda.lecar-lab.org/assets/paper/WANDA.pdf
- **Code:** coming soon