DECO: Decoupled Multimodal Diffusion Transformer for Bimanual Dexterous Manipulation with a Plugin Tactile Adapter
Paper • 2602.05513 • Published • 4
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DECO-50 is a bimanual dexterous manipulation dataset with tactile sensing, comprising 50 hours of teleoperated data across 4 scenarios and 28 subtasks, totaling over 5 million frames collected on real dual-arm robots.
DECO-50/
├── task1/
│ ├── sub_task_1/
│ │ ├── episode_000000/
│ │ │ ├── colors/
│ │ │ │ ├── 000_color_0.jpg # Left camera
│ │ │ │ ├── 000_color_1.jpg # Right camera
│ │ │ │ └── ...
│ │ │ ├── tactiles/
│ │ │ │ ├── 000_left_ee_tactile.npy # Left tactile sensor
│ │ │ │ ├── 000_right_ee_tactile.npy # Right tactile sensor
│ │ │ │ └── ...
│ │ │ └── data.json # original record file
│ │ │ └── data.pkl # processed label file
│ │ ├── episode_000001/
│ │ └── ...
│ ├── sub_task_2/
│ └── ...
├── task2/
└── ...
| Field | Description |
|---|---|
index |
Index of the data sample |
left_action |
Left arm and hand action sequence |
right_action |
Right arm and hand action sequence |
head_action |
Active head action sequence |
left_obs |
Left arm and hand observation sequence |
right_obs |
Right arm and hand observation sequence |
head_obs |
Active head observation sequence |
condition_indx |
Index of the sub-task for one-hot embedding |
You can download the dataset and use it directly with our training scripts. Please note the following:
data.pkl uses condition_indx to distinguish different object instances and task types.condition_indx in data.pkl accordingly.# Download the dataset
hf down "BAAI-Humanoid/DECO-50" --local-dir ./datasets
git clone https://github.com/BAAI-Humanoid/DECO
# Follow the instructions in https://github.com/BAAI-Humanoid/DECO?tab=readme-ov-file#2-prepare-for-dataset
If you use this dataset in your research, please cite:
@article{li2026deco,
title={DECO: Decoupled Multimodal Diffusion Transformer for Bimanual Dexterous Manipulation with a Plugin Tactile Adapter},
author={Xukun Li and Yu Sun and Lei Zhang and Bosheng Huang and Yibo Peng and Yuan Meng and Haojun Jiang and Shaoxuan Xie and Guacai Yao and Alois Knoll and Zhenshan Bing and Xinlong Wang and Zhenguo Sun},
journal={arXiv preprint arXiv:2602.05513},
year={2026}
}
This dataset is released under the Apache 2.0 license.