Add dataset card: overview, LeRobot format, usage, project page + paper links

README.md

@@ -1,3 +1,143 @@
 ---
 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-website.cmulecar.workers.dev/** · 📄 [Paper (PDF)](https://wanda-website.cmulecar.workers.dev/assets/paper/WANDA.pdf) · 💻 Code (coming soon) · 🕹️ [Interactive 4D viewer](https://wanda-website.cmulecar.workers.dev/)
+
+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-website.cmulecar.workers.dev/
+- **Paper (PDF):** https://wanda-website.cmulecar.workers.dev/assets/paper/WANDA.pdf
+- **Code:** coming soon