Create README.md

README.md

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+---
+task_categories:
+- robotics
+language:
+- en
+tags:
+- RDT
+- rdt
+- RDT 2
+- manipulation
+- bimanual
+- ur5e
+- webdatset
+- vision-language-action
+---
+
+## Dataset Summary
+
+This dataset provides  shards in the **WebDataset** format for training [RDT-2]() or policy models on **bimanual manipulation**.
+Each sample packs:
+
+* a **binocular RGB image** (left + right wrist cameras concatenated horizontally)
+* a **relative action chunk** (continuous control, 0.8s, 30Hz)
+* a **discrete action token sequence** (e.g., from an [Residual VQ action tokenizer](https://huggingface.co/robotics-diffusion-transformer/RVQActionTokenizer))
+* a **metadata JSON** with an instruction key `sub_task_instruction_key` to index corresponding instruction from `instructions.json`
+
+> Data were collected on a **bimanual UR5e** setup.
+
+---
+
+## Supported Tasks
+
+* **Instruction-conditioned bimanual manipulation **, including:
+  - Fold cloths
+  - 
+
+---
+
+## Data Structure
+
+### Shard layout
+
+Shards are named `shard-*.tar`. Inside each shard:
+
+```
+shard-000000.tar
+├── 0.image.jpg          # binocular RGB, H=384, W=768, C=3, uint8
+├── 0.action.npy         # relative actions, shape (24, 20), float32
+├── 0.action_token.npy   # action tokens, shape (27,), int16 ∈ [0, 1024]
+├── 0.meta.json          # metadata; includes "sub_task_instruction_key"
+├── 1.image.jpg
+├── 1.action.npy
+├── 1.action_token.npy
+├── 1.meta.json
+└── ...
+shard-000001.tar
+shard-000002.tar
+...
+```
+
+> **Image:** binocular wrist cameras concatenated horizontally → `np.ndarray` of shape `(384, 768, 3)` with `dtype=uint8` (stored as JPEG).
+> **Action (continuous):** `np.ndarray` of shape `(24, 20)`, `dtype=float32` (24-step chunk, 20-D control).
+> **Action tokens (discrete):** `np.ndarray` of shape `(27,)`, `dtype=int16`, values in `[0, 1024]`.
+> **Metadata:** `meta.json` contains at least `sub_task_instruction_key` pointing to an entry in top-level `instructions.json`.`
+
+
+---
+
+## Example Data Instance
+
+```json
+{
+  "image": "0.image.jpg",
+  "action": "0.action.npy",
+  "action_token": "0.action_token.npy",
+  "meta": {
+    "sub_task_instruction_key": "fold_cloth_step_3"
+  }
+}
+```
+
+---
+
+## How to Use
+
+### 1) Official Guidelines to fine-tune RDT 2s
+
+Use the example [scripts](https://github.com/thu-ml/RDT2/blob/cf71b69927f726426c928293e37c63c4881b0165/data/utils.py#L48) and [guidelines](https://github.com/thu-ml/RDT2/blob/cf71b69927f726426c928293e37c63c4881b0165/data/utils.py#L48):
+
+### 2) Minimal Loading example with 
+
+```python
+import os
+import glob
+import json
+import random
+
+import webdataset as wds
+
+
+def no_split(src):
+    yield from src
+
+def get_train_dataset(shards_dir):
+    shards = sorted(glob.glob(os.path.join(shards_dir, "shard-*.tar")))
+    random.shuffle(shards)
+    
+    num_workers = wds.utils.pytorch_worker_info()[-1]
+    workersplitter = wds.split_by_worker if len(shards) > num_workers else no_split
+    
+    assert shards, f"No shards under {shards_dir}"
+    dataset = (
+        wds.WebDataset(
+            shards,
+            shardshuffle=False,
+            nodesplitter=no_split,
+            workersplitter=workersplitter,
+            resampled=True,
+        )
+        .repeat()
+        .shuffle(8192, initial=8192)
+        .decode("pil")
+        .map(
+            lambda sample: {
+                "image": sample["image.jpg"],
+                "action_token": sample["action_token.npy"],
+                "meta": sample["meta.json"],
+            }
+        )
+        .with_epoch(nsamples=(2048 * 30 * 60 * 60))    # 2048 hours
+    )
+    
+    return dataset
+
+with open(os.path.join("<Dataset Diretory>", "instructions.json") as fp:
+    instructions = json.load(fp)
+dataset = get_train_dataset("<Dataset Diretory>")
+```
+---
+
+## Ethical Considerations
+
+* Contains robot teleoperation/automation data. No PII is present by design.
+* Ensure safe deployment/testing on real robots; follow lab safety and manufacturer guidelines.
+
+---
+
+## Citation
+
+If you use this dataset, please cite the dataset and your project appropriately. For example:
+
+```bibtex
+TBD
+```
+
+---
+
+## License
+
+* **Dataset license:** Apache-2.0 (unless otherwise noted by the maintainers of your fork/release).
+* Ensure compliance when redistributing derived data or models.
+
+---
+
+## Maintainers & Contributions
+
+We welcome fixes and improvements to the conversion scripts and docs (see https://github.com/thu-ml/RDT2/tree/main#troubleshooting).
+Please open issues/PRs with:
+
+* OS + Python versions
+* Minimal repro code
+* Error tracebacks
+* Any other helpful context
+