---
pretty_name: RawVLA-Bench
license: other
task_categories:
  - robotics
size_categories:
  - 1K<n<10K
tags:
  - robotics
  - vision-language-action
  - imitation-learning
  - raw-images
  - low-light
  - libero
  - robotwin2
---

# RawVLA-Bench

RawVLA-Bench is a paired training-trajectory dataset for studying RAW-domain
visual frontends for vision-language-action (VLA) policies. It is derived from
successful expert trajectories in **LIBERO** and **RoboTwin 2.0**.

Each trajectory has a RAW input version captured or rendered under a sampled
lighting condition and a paired default-light RGB target. The pair shares the
same trajectory identity, actions, robot states, frame count, and relative
path. Failed trajectories and replay-debug artifacts are not included.

## Five-light RAW preview

### LIBERO

![LIBERO five-light RAW agent and wrist preview](assets/libero_five_light_raw_agent_wrist.png)

The grid shows middle frames from five successful LIBERO trajectories for the
same task. Rows are agent-view and wrist-view RAW inputs; columns are the five
lighting domains and report the sampled exposure value. Pixels use the stored
sensor-domain `uint8` values directly, without per-image normalization or
brightness enhancement, so the low-light inputs intentionally appear dark.

### RoboTwin 2.0

![RoboTwin 2.0 five-light RAW agent and dual-wrist preview](assets/robotwin2_five_light_raw_agent_dual_wrist.png)

This grid uses five successful `grab_roller` trajectories. Rows are the agent,
left-wrist, and right-wrist RAW inputs; columns are the five lighting domains.
The stored linear `float32 [0,1]` values are clipped to that fixed range and
mapped to `uint8` only for display, with no per-image normalization or
brightness enhancement.

## Dataset contents

| Source | Successful paired trajectories | Views | RAW representation |
|---|---:|---|---|
| LIBERO | 1,771 | agent view, wrist | `uint8`, 3-channel sensor-domain representation |
| RoboTwin 2.0 | 632 | head, left, right | `float32 [0,1]`, 3-channel linear sensor-domain representation |
| **Total** | **2,403** | | |

There are 4,806 NPZ files: one RAW file and one RGB target file for each of the
2,403 successful trajectories.

```text
data/
├── libero/
│   ├── raw/<suite>/episode_XXXXXX/variant_00.npz
│   └── rgb/<suite>/episode_XXXXXX/variant_00.npz
└── robotwin2/
    ├── raw/<task>/episode_XXXXXX.npz
    └── rgb/<task>/episode_XXXXXX.npz
```

Pair files by their path relative to `raw/` or `rgb/`.

## LIBERO subset

The LIBERO subset covers `libero_spatial`, `libero_object`, `libero_goal`, and
`libero_10`. Demonstrations were replayed from the per-demonstration model XML
and HDF5 initial state, with the standard 10 dummy steps and OpenVLA no-op
filtering. The RAW and default-light RGB versions use the same saved actions,
states, frame selection, and camera convention.

All 1,771 published episodes reached success in the original OpenVLA/OFT
replay-generation environment. A separate audit in a newer StarVLA simulator
environment reproduced 1,474 successes; the other 297 were rechecked in the
generation environment and succeeded, indicating simulator-version drift
rather than failed generated trajectories.

LIBERO RAW files contain:

- `agentview_raw_uint8`, `wrist_raw_uint8`
- `actions`, `states`, `robot_states`, `rewards`, `dones`
- `metadata_json`

The corresponding RGB files replace the image fields with
`agentview_rgb_uint8` and `wrist_rgb_uint8`.

## RoboTwin 2.0 subset

The RoboTwin subset begins from the strict success-only allowlist produced by
clean closed-loop expert replay. Inclusion required a completed replay,
`success=true`, an existing replay HDF5 file, available PSNR/SSIM metrics, and
matching original/replay frame counts for all three cameras.

The allowlist contains 633 successful episodes across 13 tasks. One allowlisted
episode, `put_bottles_dustbin/episode_000031`, failed during paired lighting
export and is omitted. The published subset therefore contains **632 complete
RAW/RGB pairs** and no task-failure episodes.

For RAW generation, the clean successful replay's physics state was copied at
each frame and rendered under the sampled lighting condition without advancing
the perturbed environment with actions. This preserves alignment with the
successful clean trajectory.

RoboTwin RAW files contain:

- `head_camera_raw_float32`, `left_camera_raw_float32`,
  `right_camera_raw_float32`
- `actions`, `robot_states`, `left_endpose`, `right_endpose`, `rewards`, `dones`
- `metadata_json`

The corresponding RGB files replace the image fields with
`head_camera_rgb_uint8`, `left_camera_rgb_uint8`, and
`right_camera_rgb_uint8`.

## Loading an episode

```python
import numpy as np

raw_path = "data/libero/raw/libero_spatial_no_noops_1.0.0_lerobot/episode_000000/variant_00.npz"
rgb_path = raw_path.replace("/raw/", "/rgb/")

with np.load(raw_path, allow_pickle=False) as raw, np.load(rgb_path, allow_pickle=False) as rgb:
    actions = raw["actions"]
    raw_frames = raw["agentview_raw_uint8"]
    target_frames = rgb["agentview_rgb_uint8"]
```

NPZ files are episode-level and may be large. Load one episode at a time or use
memory-aware preprocessing when constructing frame or sequence datasets.

## Intended use

RawVLA-Bench is intended for research on RAW-to-RGB or RAW-to-feature
frontends, illumination robustness, paired representation learning, and VLA
policy training. It contains simulation data and should not be interpreted as
evidence of real-world robot safety or deployment readiness.

This release contains training trajectories only. It does not define a held-out
test split or a leaderboard metric.

## Licenses and attribution

This is a mixed-license derived dataset:

- The LIBERO-derived subset retains the upstream **Creative Commons
  Attribution 4.0 International (CC BY 4.0)** dataset license and attribution
  requirements.
- The RoboTwin 2.0-derived subset follows the upstream **MIT** license.

Users are responsible for complying with the applicable upstream license for
each subset. Please also cite the corresponding source benchmark.

## Citations

```bibtex
@article{liu2023libero,
  title={LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning},
  author={Liu, Bo and Zhu, Yifeng and Gao, Chongkai and Feng, Yihao and Liu, Qiang and Zhu, Yuke and Stone, Peter},
  journal={arXiv preprint arXiv:2306.03310},
  year={2023}
}

@article{chen2025robotwin,
  title={RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation},
  author={Chen, Tianxing and Chen, Zanxin and Chen, Baijun and Cai, Zijian and Liu, Yibin and others},
  journal={arXiv preprint arXiv:2506.18088},
  year={2025}
}
```

## Upstream resources

- LIBERO: https://libero-project.github.io/
- LIBERO demonstrations: https://huggingface.co/datasets/yifengzhu-hf/LIBERO-datasets
- RoboTwin 2.0: https://robotwin-platform.github.io/
- RoboTwin 2.0 dataset: https://huggingface.co/datasets/TianxingChen/RoboTwin2.0