---
configs:
- config_name: preview
  data_files:
  - split: train
    path:
    - "preview/train/*.mp4"
    - "preview/train/metadata.jsonl"
---

# iamlab_cmu_pickup_insert_converted_externally_to_rlds robot-removal inpainting dataset

This dataset contains robot-removal inpainting results for **iamlab_cmu_pickup_insert_converted_externally_to_rlds**. Each episode provides:
- `inpainting.mp4`: the robot visually removed via inpainting
- `mask.mp4`: the robot mask video used for inpainting
- `original_episode.mp4`: the original (unmodified) episode video
- `language_instructions_{split}_all.txt`: tab-separated mapping from `episode_id` to instruction

## Relation to OXE-AugE

This release is produced as part of **OXE-AugE** (AugE-Toolkit), a large-scale robot augmentation project.
This inpainting dataset is an intermediate artifact from the overall augmentation pipeline, released independently because it is valuable for downstream research and reuse.

## Folder structure

```
iamlab_cmu_pickup_insert_converted_externally_to_rlds/
├── README.md
├── archives/
│   └── iamlab_cmu_pickup_insert_converted_externally_to_rlds_train.tar
├── preview/
│   └── train/
│       ├── 000000_inpainting.mp4
│       ├── 000000_mask.mp4
│       ├── 000000_original.mp4
│       ├── ...
│       └── metadata.jsonl
└── language_instructions_train_all.txt
```

## How to extract

From the dataset repo root:

```bash
tar -xf archives/iamlab_cmu_pickup_insert_converted_externally_to_rlds_train.tar
```

Each tar extracts to:

```
iamlab_cmu_pickup_insert_converted_externally_to_rlds/
└── {split}/
    └── {episode_id}/
        ├── inpainting.mp4
        ├── mask.mp4
        └── original_episode.mp4
```

## Instruction mapping

For each processed split, `language_instructions_{split}_all.txt` contains lines:

```
<episode_id>\t<instruction>
```

(`\t` means a literal TAB character.)

So episode `17` corresponds to the line starting with `17\t...`, and to folder:
`iamlab_cmu_pickup_insert_converted_externally_to_rlds/{split}/17/`

## Citation

```bibtex
@misc{
  ji2025oxeauge,
  title  = {OXE-AugE: A Large-Scale Robot Augmentation of OXE for Scaling Cross-Embodiment Policy Learning},
  author = {Ji, Guanhua and Polavaram, Harsha and Chen, Lawrence Yunliang and Bajamahal, Sandeep and Ma, Zehan and Adebola, Simeon and Xu, Chenfeng and Goldberg, Ken},
  journal = {arXiv preprint arXiv:2512.13100},
  year = {2025}
}
```