Datasets:
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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
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