---
title: RoboDojo-Aug
canonical_url: "https://www.modelscope.cn/datasets/N0ct1s/RoboDojo-Aug"
md_url: "https://www.modelscope.cn/datasets/N0ct1s/RoboDojo-Aug.md"
repository: N0ct1s/RoboDojo-Aug
last_updated: 2026-10-04
license: Apache-2.0
downloads: 7220
stars: 0
---

# RoboDojo-Aug

> RoboDojo-Aug - N0ct1s 在 ModelScope 开源的数据集。RoboDojo augmentation data (coverblocks, fastenscrews)

N0ct1s/RoboDojo-Aug 是 ModelScope 魔搭社区上的数据集，采用 Apache-2.0 许可。

- **Repository**: N0ct1s/RoboDojo-Aug
- **License**: Apache-2.0
- **Downloads**: 7220
- **Stars**: 0
- **Last updated**: 2026-10-04

Source: https://www.modelscope.cn/datasets/N0ct1s/RoboDojo-Aug

---

# RoboDojo augmentation data (cover_blocks, fasten_screws)

Simulated bimanual demonstrations for two RoboDojo tasks, **generated** (not recorded by the RoboDojo authors) by
transforming segments of the released RoboDojo demonstrations to new object layouts and re-planning the motion in
the RoboDojo / Isaac Sim environment, then checking every episode with the official RoboDojo success check.

**Not official RoboDojo data.** Source and environment:
- RoboDojo benchmark: https://github.com/RoboDojo-Benchmark/RoboDojo
- Released RoboDojo demonstrations: https://huggingface.co/datasets/RoboDojo-Benchmark/RoboDojo (Apache-2.0)

No evaluation layouts or evaluation seeds of the benchmark were used to generate this data.

## Layout
`shards/<task>/<shard>/`, each shard about 50 source episodes:
- `full/` LeRobot v3.0 dataset: whole episodes, official task instruction, clean images (same format and video
  encoding as the released RoboDojo LeRobot data: 25 fps, AV1 CRF 18, g 2, yuv420p).
- `subtask/` LeRobot v3.0 dataset: one episode per atomic skill (pick, insert, tighten, cover, uncover, hand-over,
  regrasp, reseat, return_home, ...), sub-task instruction, clean images plus the exact target mask of every camera
  (`observation.masks.<cam>`, lossless PNG). The mask overlay used for training is applied at data loading.
- every source episode appears twice: the standard appearance and one random appearance (room, table / floor
  material, lighting, visual-only distractors); both replay the identical motion.
- about 10 % are corner cases (an injected error and its recovery); the injected segment itself is in no dataset,
  full episodes of corner cases start where the injection ended.
- `aug/episodes.jsonl` generation metadata (layout, seeds, source segments, atomic-skill records),
  `aug/commands/*.npz` commanded actions and object poses (replaying them reproduces the episode),
  `attempts.jsonl` all generation attempts, `shard.json` machine / GPU / seeds, `manifest.json` sizes and SHA-256.