robotics-diffusion-transformer/BimanualUR5eExample

Dataset Summary

This dataset provides shards in the WebDataset format for fine-tuning RDT-2 or other 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)
• 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.

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Supported Tasks

• Instruction-conditioned bimanual manipulation, including:
  • Pouring water: different water bottles and cups
  • Cleaning the desktop: different dustpans and paper balls
  • Folding towels: towels of different sizes and colors
  • Stacking cups: cups of different sizes and colors

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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.

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Example Data Instance

{
  "image": "0.image.jpg",
  "action": "0.action.npy",
  "action_token": "0.action_token.npy",
  "meta": {
    "sub_task_instruction_key": "fold_cloth_step_3"
  }
}

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How to Use

1) Official Guidelines to fine-tune RDT 2 series

Use the example scripts and guidelines:

2) Minimal Loading example

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(os.path.join("<Dataset Diretory>", "shards"))

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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.

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Citation

If you use this dataset, please cite the dataset and your project appropriately. For example:

@software{rdt2,
    title={RDT2: Enabling Zero-Shot Cross-Embodiment Generalization by Scaling Up UMI Data},
    author={RDT Team},
    url={https://github.com/thu-ml/RDT2},
    month={September},
    year={2025}
}

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License

• Dataset license: Apache-2.0 (unless otherwise noted by the maintainers of your fork/release).
• Ensure compliance when redistributing derived data or models.

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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