Datasets:

SimpleJev
/

JevAny-Robotics-Train-v0.4

---
license: other
license_name: mixed-source-licenses-non-commercial
license_link: LICENSE.md
pretty_name: JevAny-Robotics-Train v0.4
task_categories:
- video-classification
- image-classification
- visual-question-answering
tags:
- robotics
- reward-model
- judge
- jevany
- progress-estimation
- failure-detection
size_categories:
- 100K<n<1M
viewer: false
extra_gated_prompt: >-
  JevAny-Robotics-Train v0.4 is for non-commercial research only. It is a compilation of other datasets: the license of
  each record's source applies to it (see LICENSE.md), and several sources are licensed CC BY-NC-SA 4.0 or CC BY-NC 4.0.
  Access is granted automatically once you agree.
extra_gated_fields:
  I will use this dataset only for non-commercial research: checkbox
  I will follow the license of each source dataset listed in the LICENSE file: checkbox
extra_gated_button_content: Agree and access
---
# JevAny-Robotics-Train v0.4

The training set of [`SimpleJev/JevAny-Robotics-4B-LoRA`](https://huggingface.co/SimpleJev/JevAny-Robotics-4B-LoRA) v0.4:
judgement questions about robot manipulation (task progress, 1–5 rollout scores, success, failure mode, plan checking, failure
localisation, trajectory preference) over real and simulated robot images and videos, plus general replay questions. It is built
only from the train side of public datasets and decontaminated against RoboPulse, RoboPulse++, RoboRewardBench and
JevAny-Robotics-Public.

**Size:** 298,222 questions (train 286,795, calibration 2,279, development 9,148) and 704,700 images and video clips
(50.8 GB in 21 tar shards).

**Identical to the model's training set except for VLAC-Cut.** VLAC-Cut's 2,650 train and val rows and the 342-row RoboPulse++
threshold set are withheld, because the VLAC-Cut repository grants no license. [`vlaccut_rebuild/`](#rebuilding-the-vlac-cut-rows)
recreates them from your own copy of VLAC-Cut. Every other row is byte-identical to what the model was trained on, and after
the rebuild every file is.

> **Non-commercial use only.** This dataset compiles other datasets, and the license of each record's source (`_meta.source`,
> `_meta.origin`; table below) applies to the material derived from it. Several sources are non-commercial: airoa-moma,
> AgiBot World 2026 and ScienceQA are CC BY-NC-SA 4.0, and RH20T scenes 6–10 are CC BY-NC 4.0. Our own annotations (question
> wording, constructed options and labels) are released under CC BY-NC-SA 4.0. See [`LICENSE.md`](LICENSE.md).

## Download

Accept the terms on this page, log in (`hf auth login`), then:

```bash
hf download SimpleJev/JevAny-Robotics-Train-v0.4 --repo-type dataset --local-dir JevAny-Robotics-Train-v0.4
cd JevAny-Robotics-Train-v0.4 && bash unpack_media.sh   # 51 GB of media; then the shards can be deleted
```

Records refer to their media as `media/<source>/<file>`, relative to this folder. The folder is a JevAny suite:
`jevany.suite.load_split(folder, "train")` checks each file against `manifest.json`.

| File | Contents |
|---|---|
| `train.jsonl` | 286,795 training questions |
| `calibration.jsonl`, `development.jsonl` | 2,279 and 9,148 held-out questions, whose groups (task, scene or session) never appear in train |
| `val.jsonl` | calibration + development (11,427) |
| `media/media-000NN.tar` | 21 uncompressed tar shards with 704,700 files; `media_files.tsv` maps every file to its shard, `media_shards.json` gives each shard's size and sha256 |
| `unpack_media.sh` | unpacks the shards and checks that every referenced file exists |
| `manifest.json` | JevAny suite manifest: sha256 and record count of each file |
| `stats.json`, `samples.md` | counts by source, family and target type; one example per family and file |
| `build_manifest.json`, `verify.json` | how the set was assembled; the checks run on this release |
| `vlaccut_rebuild/` | script, keyframe list and our instruction translations for rebuilding the VLAC-Cut rows |

## Sources and licenses

Val = calibration + development. Upstream names are Hugging Face dataset repositories.

| Source (`_meta.source`) | Train | Val | License | Upstream |
|---|---:|---:|---|---|
| Robo-Dopamine GRM data (`grm`) | 80,752 | 1,592 | Apache-2.0 ¹ | `tanhuajie2001/Robo-Dopamine-GRM-Dataset` |
| RoboReward, train split (`roboreward`) | 61,788 | 2,700 | CC BY 4.0 | `teetone/RoboReward` |
| DenseReward (`densereward`) | 27,974 | 1,892 | Apache-2.0 | `densereward/DenseReward` |
| Guardian, RLBench (`guardian_rlbench`) | 23,557 | 500 | Apache-2.0 | `paulpacaud/rlbenchfail_train_dataset` |
| Guardian, BridgeData V2 (`guardian_bdv2`) | 15,961 | 948 | Apache-2.0 | `paulpacaud/bdv2fail_train_dataset` |
| ViFailback (`vifailback`) | 9,566 | 817 | MIT | `sii-rhos-ai/ViFailback-Dataset` |
| airoa-moma (`airoa`) | 9,471 | 1,022 | **CC BY-NC-SA 4.0** | `airoa-org/airoa-moma` |
| RoboVQA (`robovqa`) | 7,997 | 500 | CC BY 4.0 | RoboVQA, via `Tianli/robovqa` |
| RoboFAC (`robofac`) | 6,548 | 843 | MIT | `MINT-SJTU/RoboFAC-dataset` |
| RoboCOIN (`robocoin`) | 3,602 | 170 | Apache-2.0 | 340 `RoboCOIN/*` repositories |
| REASSEMBLE, train split (`reassemble`) | 1,845 | 60 | CC BY 4.0 | `robot-lev/reassemble` |
| AgiBot World 2026, RL rollouts (`agibot`) | 1,680 | 195 | **CC BY-NC-SA 4.0** | `agibot-world/AgiBotWorld2026` |
| RH20T, configurations 3–4 (`rh20t`), scenes 1–5 | 641 | 24 | CC BY-SA 4.0 | `robot-lev/rh20t_cfg3`, `robot-lev/rh20t_cfg4` |
| RH20T, configurations 3–4 (`rh20t`), scenes 6–10 | 639 | 13 | **CC BY-NC 4.0** | same |
| RoboArena DataDump 2026-07-17 (`roboarena`) | 1,136 | 70 | MIT | `RoboArena/DataDump_07-17-2026` |
| RoboMIND, failure data (`robomind`) | 969 | 81 | Apache-2.0 | `x-humanoid-robomind/RoboMIND` |
| VLAC-Cut, ARX data (`vlaccut`) | *2,516 withheld* | *134 withheld* | no license terms | `InternRobotics/VLAC-Cut-Benchmark` |
| **General replay** | | | | |
| HelpSteer3 (`helpsteer3`) | 13,267 | – | CC BY 4.0 | `nvidia/HelpSteer3` |
| A-OKVQA (`aokvqa`) | 4,990 | – | see ² | `HuggingFaceM4/A-OKVQA` |
| Hermes function calling (`agent_tool`) | 4,502 | – | Apache-2.0 | `NousResearch/hermes-function-calling-v1` |
| ScienceQA (`scienceqa`) | 3,410 | – | **CC BY-NC-SA 4.0** ³ | `derek-thomas/ScienceQA` |
| CommonsenseQA (`commonsense`) | 2,994 | – | MIT | `tau/commonsense_qa` |
| QASC (`qasc`) | 2,662 | – | CC BY 4.0 | `allenai/qasc` |
| VideoFeedback (`video_feedback`) | 500 | – | Apache-2.0 | `TIGER-Lab/VideoFeedback` |
| ARC-Challenge (`arc`) | 344 | – | CC BY-SA 4.0 | `allenai/ai2_arc` |

¹ GRM's images come from AgiBot World, AgileX, DROID, EgoDex, Galaxea, LIBERO, Pika, RoboCasa, RoboTwin and dual-arm Franka
data; those datasets' own terms may also apply, and some are non-commercial. The upstream repository asks you to accept its
terms. ² The A-OKVQA repository (`allenai/aokvqa`) is Apache-2.0; its images are COCO 2017 images, whose use follows the Flickr
terms of use. ³ The original ScienceQA release is CC BY-NC-SA 4.0; the mirror we used lists CC BY-SA 4.0, and so does `_meta.license`
in those rows. Where `_meta.license` (kept byte-identical to the training set) differs from this table, follow the table.

Each row's `_meta.source` and `_meta.license` make it easy to drop sources and retrain. Dropping the non-commercial sources
does not make the rest safe for commercial use: GRM's images come from datasets with their own terms.

## What is asked

| Target type | Train | Calibration | Development | Question |
|---|---:|---:|---:|---|
| `robopulse` | 88,751 | 281 | 1,711 | is the AFTER observation closer to the goal than the BEFORE one, given start and end references (RoboPulse style) |
| `rrb` | 44,917 | 392 | 1,608 | 1–5 progress score of a rollout video (RoboRewardBench style) |
| `replay` | 32,669 | – | – | general text, image and tool-use judgement |
| `jj_success` | 32,433 | 361 | 1,246 | did the step or trajectory succeed |
| `jj_mode_plan` | 30,933 | 396 | 1,455 | failure mode; is a plan correct; what is wrong with a plan |
| `robopulsepp` | 26,873 | 525 | 1,869 | did progress rise or fall between consecutive observations, 1–3 camera views (RoboPulse++ style) |
| `jj_pref` | 19,533 | 144 | 696 | which of two trajectories is better |
| `jj_loc` | 10,686 | 180 | 563 | at which step or frame the failure happens |

`jj_*` follow the task types of JevAny-Robotics-Public. Questions are yes/no (`noul`, 170,202), multiple choice (`choice`, 85,105)
or 1–5 scores (`score`, 47,417). Labels come from the source's human annotation (`human_expert`, 62,203; `human_multi`, 13,767),
from how the question was built (`construction`, 167,099: for example time order, or a plan we perturbed), from RoboReward's
AI-assisted labels (`ai_assisted`, 55,079) or from an AI check of human failure labels (`ai_verified`, 74).

<details>
<summary>By family</summary>

| Family | Train | Calibration | Development | Label source |
|---|---:|---:|---:|---|
| `grm_rp_pair` | 80,752 | 199 | 1,393 | construction |
| `roboreward_train_progress` | 44,917 | 392 | 1,608 | ai_assisted, human_expert |
| `densereward_pp_step` | 19,975 | 521 | 971 | construction |
| `roboreward_train_traj_pref` | 16,871 | 128 | 572 | ai_assisted, human_expert |
| `replay_helpsteer3` | 13,267 | – | – | human_multi |
| `guardian_rlbench_exec_success` | 10,846 | 100 | 100 | construction |
| `densereward_rp_pair` | 7,999 | 82 | 318 | construction |
| `robovqa_train_exec_success` | 7,997 | 99 | 401 | human_expert |
| `airoa_rpp_pair` | 6,778 | – | 892 | human_expert, construction |
| `guardian_bdv2_exec_success` | 6,276 | 54 | 313 | construction |
| `guardian_rlbench_exec_failure_mode` | 5,400 | 38 | 37 | construction |
| `replay_aokvqa` | 4,990 | – | – | human_expert |
| `guardian_rlbench_plan_correct` | 4,876 | 76 | 74 | construction |
| `replay_agent_tool` | 4,502 | – | – | human_expert |
| `guardian_bdv2_plan_correct` | 4,315 | 48 | 242 | construction |
| `robofac_failure_step` | 3,768 | – | 343 | construction |
| `vifailback_failure_step` | 3,460 | 90 | 110 | human_expert |
| `vifailback_failure_frame` | 3,458 | 90 | 110 | human_expert |
| `replay_scienceqa` | 3,410 | – | – | human_expert |
| `guardian_bdv2_exec_failure_mode` | 3,195 | 23 | 122 | construction |
| `replay_commonsense` | 2,994 | – | – | human_expert |
| `replay_qasc` | 2,662 | – | – | human_expert |
| `guardian_rlbench_plan_failure_mode` | 2,435 | 38 | 37 | construction |
| `guardian_bdv2_plan_failure_mode` | 2,175 | 24 | 122 | construction |
| `robocoin_plan_correct` | 1,871 | 6 | 82 | human_expert, construction |
| `vifailback_failure_type` | 1,500 | 137 | 158 | human_expert |
| `robofac_failure_type` | 1,500 | – | 300 | construction |
| `agibot_exec_failure_mode` | 1,471 | – | 156 | human_expert |
| `robofac_traj_success` | 1,280 | 42 | 158 | construction |
| `roboarena_traj_pref` | 1,136 | 10 | 60 | human_expert |
| `vifailback_success` | 1,084 | 47 | 65 | human_expert |
| `airoa_traj_success` | 1,042 | – | 52 | human_expert |
| `airoa_exec_success` | 1,040 | – | 52 | human_expert |
| `robomind_exec_failure_mode` | 969 | – | 81 | human_expert |
| `robocoin_plan_failure_mode` | 944 | 4 | 40 | construction |
| `reassemble_traj_success` | 738 | 6 | 18 | human_expert |
| `reassemble_exec_success` | 738 | 5 | 19 | human_expert |
| `robocoin_traj_success` | 526 | – | 26 | human_expert, construction |
| `airoa_traj_pref` | 521 | – | 26 | human_expert |
| `replay_video_feedback` | 500 | – | – | human_multi |
| `rh20t_traj_success` | 382 | 4 | 8 | human_expert |
| `rh20t_exec_success` | 382 | 4 | 8 | human_expert |
| `reassemble_traj_pref` | 369 | 1 | 11 | human_expert |
| `replay_arc` | 344 | – | – | human_expert |
| `rh20t_traj_pref` | 324 | 2 | 5 | human_expert |
| `robocoin_traj_pref` | 261 | 3 | 9 | construction |
| `rh20t_exec_failure_mode` | 192 | 2 | 4 | human_expert |
| `agibot_traj_success` | 102 | – | 26 | human_expert, construction |
| `vifailback_rpp_pair` | 64 | 4 | 6 | ai_verified |
| `airoa_plan_correct` | 60 | – | – | human_expert, construction |
| `agibot_rpp_pair` | 56 | – | – | human_expert |
| `agibot_traj_pref` | 51 | – | 13 | construction |
| `airoa_plan_failure_mode` | 30 | – | – | construction |

GRM by dataset family (train): AgiBot 10,941, AgileX 10,941, DROID 10,941, EgoDex 10,941, RoboTwin 10,935, Galaxea 8,000,
RoboCasa 8,000, LIBERO 4,803, Pika 2,910, dual-arm Franka 2,340.

</details>

## Record format

One JSON object per line, in JevAny's System One format. `state` is what the judge is shown, `questions.decision` the question
and its label, `media` the attached images or videos, and `_meta` where the record comes from. A RoboRewardBench-style record,
with the instructions and criteria shortened:

```json
{"state": {"task": "Pick fruit", "video": "the rollout (first to last frame)"},
 "questions": {"decision": {"type": "score",
   "instructions": "You are watching one rollout of a robot attempting a manipulation task. ... Rate the robot's end-of-episode progress on the task on a 1-5 scale ...",
   "criteria": ["1 - No success: ...", "2 - Minimal progress: ...", "3 - Partial completion: ...", "4 - Near completion: ...", "5 - Perfect completion: ..."],
   "label": 0, "src": "roboreward_train_progress"}},
 "media": [{"type": "video", "uri": "media/roboreward/99584eef89e6a37539f3839c.mp4"}],
 "_meta": {"id": "roboreward/train/05252", "group_id": "roboreward/berkeley_mvp_converted_externally_to_rlds/pick fruit",
   "source": "roboreward", "origin": "teetone/RoboReward@469b9af76e8539e8d2ac553b081307738fd92ca5",
   "family": "roboreward_train_progress", "label_source": "ai_assisted", "license": "CC-BY-4.0", "split": "train",
   "target_type": "rrb", "extra": {"subset": "berkeley_mvp_converted_externally_to_rlds", "reward": 1, "...": "..."},
   "n_media": 1, "approx_tokens": 192, "...": "..."}}
```

- `label` is `true` / `false` for `noul`, the key of the right option in `criteria` for `choice`, and the score minus one for
  `score` (0 = score 1).
- `_meta.group_id` is the unit of the train / val split (task, scene or session); `_meta.origin` is `repository@revision`.
- 4,502 tool-use records (`agent_tool`) carry a second question.

## How it was built

- Only the train side of each source; no item of any benchmark's test or eval split.
- Decontamination against RoboPulse, RoboPulse++, RoboRewardBench and JevAny-Robotics-Public: their episodes are blocked by key,
  and every image and video frame (2 fps) is pHash-checked against 168,861 frames of those benchmarks. A match within Hamming
  distance 4, confirmed by a pixel MAD below 6/255, removes the whole episode or session.
- RoboReward rows follow the official split: they share no episode with the RoboRewardBench test set, but they are not
  frame-disjoint from it (3,771 rows have a frame that matches a test video of the same scene).
- Calibration and development are held out from train by group and were never trained on.

**Known label caveats.** RoboReward clips cut before the end of an episode carry labels that a careful annotator reproduces only
to within ±1 (20% exactly, 80% within one point). RoboReward's RoboArena-derived scores give credit for attempting. Instruction
length alone predicts a RoboReward score below 5 (AUROC 0.70 on train, 0.68 on the RoboRewardBench test), because
counterfactual instructions are longer.

## Rebuilding the VLAC-Cut rows

[`InternRobotics/VLAC-Cut-Benchmark`](https://huggingface.co/datasets/InternRobotics/VLAC-Cut-Benchmark) is public, but its
LICENSE is a placeholder, so we redistribute neither its frames nor its annotations. Its ARX rows (2,516 train, 33 calibration,
101 development and the 342-row `rpp_threshold_dev.jsonl`) are RoboPulse++-style pairs of consecutive human keyframes,
including 1,258 real falls in train (363 of them with several views). To rebuild them:

```bash
hf download InternRobotics/VLAC-Cut-Benchmark splits/train.json data/train_videos.tar --repo-type dataset \
    --revision fd312e11643bfd7d747a76cad57f0330c4cc0ec0 --local-dir VLAC-Cut-Benchmark   # about 90 GB
pip install av pillow numpy
python vlaccut_rebuild/rebuild_vlaccut.py --vlac VLAC-Cut-Benchmark --workers 8          # about 16 GB of RAM
```

The script reads the instruction, progress values and action description of each keyframe from your copy, decodes the 7,733
frames, and writes the five full JSONL files with every row at its original line, plus the original `manifest.json` (the
released files are kept as `*.without_vlaccut.*`). It then checks each file against the sha256 of the training set
(`vlaccut_rebuild/original_files.json`). From this folder it uses only the keyframe pairs and views we chose
(`vlaccut_index.jsonl`) and our English translations of the 688 instructions (`instructions_en.json`). On our machine it
reproduced every file and every frame byte for byte; other FFmpeg or Pillow builds may encode some frames slightly differently,
which changes no row.

## Verification

Run on this folder before upload (`verify.json`):

- JevAny `load_split` passes for train, calibration and development; every source is declared in `manifest.json`.
- Every line equals the training-set line, in order, with only the VLAC-Cut lines removed.
- No duplicate ids; no group shared between train and val.
- The 704,700 referenced media files and the files in the 21 shards are the same set, with matching sizes and shard sha256.

## Acknowledgements

This dataset exists because the authors of the datasets above released their data. Please cite them when you use the
corresponding parts. The model trained on it, its evaluation and its full acknowledgements are on the
[model card](https://huggingface.co/SimpleJev/JevAny-Robotics-4B-LoRA).

	---
	license: other
	license_name: mixed-source-licenses-non-commercial
	license_link: LICENSE.md
	pretty_name: JevAny-Robotics-Train v0.4
	task_categories:
	- video-classification
	- image-classification
	- visual-question-answering
	tags:
	- robotics
	- reward-model
	- judge
	- jevany
	- progress-estimation
	- failure-detection
	size_categories:
	- 100K<n<1M
	viewer: false
	extra_gated_prompt: >-
	JevAny-Robotics-Train v0.4 is for non-commercial research only. It is a compilation of other datasets: the license of
	each record's source applies to it (see LICENSE.md), and several sources are licensed CC BY-NC-SA 4.0 or CC BY-NC 4.0.
	Access is granted automatically once you agree.
	extra_gated_fields:
	I will use this dataset only for non-commercial research: checkbox
	I will follow the license of each source dataset listed in the LICENSE file: checkbox
	extra_gated_button_content: Agree and access
	---

	# JevAny-Robotics-Train v0.4

	The training set of [`SimpleJev/JevAny-Robotics-4B-LoRA`](https://huggingface.co/SimpleJev/JevAny-Robotics-4B-LoRA) v0.4:
	judgement questions about robot manipulation (task progress, 1–5 rollout scores, success, failure mode, plan checking, failure
	localisation, trajectory preference) over real and simulated robot images and videos, plus general replay questions. It is built
	only from the train side of public datasets and decontaminated against RoboPulse, RoboPulse++, RoboRewardBench and
	JevAny-Robotics-Public.

	Size: 298,222 questions (train 286,795, calibration 2,279, development 9,148) and 704,700 images and video clips
	(50.8 GB in 21 tar shards).

	Identical to the model's training set except for VLAC-Cut. VLAC-Cut's 2,650 train and val rows and the 342-row RoboPulse++
	threshold set are withheld, because the VLAC-Cut repository grants no license. [`vlaccut_rebuild/`](#rebuilding-the-vlac-cut-rows)
	recreates them from your own copy of VLAC-Cut. Every other row is byte-identical to what the model was trained on, and after
	the rebuild every file is.

	> Non-commercial use only. This dataset compiles other datasets, and the license of each record's source (`_meta.source`,
	> `_meta.origin`; table below) applies to the material derived from it. Several sources are non-commercial: airoa-moma,
	> AgiBot World 2026 and ScienceQA are CC BY-NC-SA 4.0, and RH20T scenes 6–10 are CC BY-NC 4.0. Our own annotations (question
	> wording, constructed options and labels) are released under CC BY-NC-SA 4.0. See [`LICENSE.md`](LICENSE.md).

	## Download

	Accept the terms on this page, log in (`hf auth login`), then:

	```bash
	hf download SimpleJev/JevAny-Robotics-Train-v0.4 --repo-type dataset --local-dir JevAny-Robotics-Train-v0.4
	cd JevAny-Robotics-Train-v0.4 && bash unpack_media.sh # 51 GB of media; then the shards can be deleted
	```

	Records refer to their media as `media/<source>/<file>`, relative to this folder. The folder is a JevAny suite:
	`jevany.suite.load_split(folder, "train")` checks each file against `manifest.json`.

	\| File \| Contents \|
	\|---\|---\|
	\| `train.jsonl` \| 286,795 training questions \|
	\| `calibration.jsonl`, `development.jsonl` \| 2,279 and 9,148 held-out questions, whose groups (task, scene or session) never appear in train \|
	\| `val.jsonl` \| calibration + development (11,427) \|
	\| `media/media-000NN.tar` \| 21 uncompressed tar shards with 704,700 files; `media_files.tsv` maps every file to its shard, `media_shards.json` gives each shard's size and sha256 \|
	\| `unpack_media.sh` \| unpacks the shards and checks that every referenced file exists \|
	\| `manifest.json` \| JevAny suite manifest: sha256 and record count of each file \|
	\| `stats.json`, `samples.md` \| counts by source, family and target type; one example per family and file \|
	\| `build_manifest.json`, `verify.json` \| how the set was assembled; the checks run on this release \|
	\| `vlaccut_rebuild/` \| script, keyframe list and our instruction translations for rebuilding the VLAC-Cut rows \|

	## Sources and licenses

	Val = calibration + development. Upstream names are Hugging Face dataset repositories.

	\| Source (`_meta.source`) \| Train \| Val \| License \| Upstream \|
	\|---\|---:\|---:\|---\|---\|
	\| Robo-Dopamine GRM data (`grm`) \| 80,752 \| 1,592 \| Apache-2.0 ¹ \| `tanhuajie2001/Robo-Dopamine-GRM-Dataset` \|
	\| RoboReward, train split (`roboreward`) \| 61,788 \| 2,700 \| CC BY 4.0 \| `teetone/RoboReward` \|
	\| DenseReward (`densereward`) \| 27,974 \| 1,892 \| Apache-2.0 \| `densereward/DenseReward` \|
	\| Guardian, RLBench (`guardian_rlbench`) \| 23,557 \| 500 \| Apache-2.0 \| `paulpacaud/rlbenchfail_train_dataset` \|
	\| Guardian, BridgeData V2 (`guardian_bdv2`) \| 15,961 \| 948 \| Apache-2.0 \| `paulpacaud/bdv2fail_train_dataset` \|
	\| ViFailback (`vifailback`) \| 9,566 \| 817 \| MIT \| `sii-rhos-ai/ViFailback-Dataset` \|
	\| airoa-moma (`airoa`) \| 9,471 \| 1,022 \| CC BY-NC-SA 4.0 \| `airoa-org/airoa-moma` \|
	\| RoboVQA (`robovqa`) \| 7,997 \| 500 \| CC BY 4.0 \| RoboVQA, via `Tianli/robovqa` \|
	\| RoboFAC (`robofac`) \| 6,548 \| 843 \| MIT \| `MINT-SJTU/RoboFAC-dataset` \|
	\| RoboCOIN (`robocoin`) \| 3,602 \| 170 \| Apache-2.0 \| 340 `RoboCOIN/*` repositories \|
	\| REASSEMBLE, train split (`reassemble`) \| 1,845 \| 60 \| CC BY 4.0 \| `robot-lev/reassemble` \|
	\| AgiBot World 2026, RL rollouts (`agibot`) \| 1,680 \| 195 \| CC BY-NC-SA 4.0 \| `agibot-world/AgiBotWorld2026` \|
	\| RH20T, configurations 3–4 (`rh20t`), scenes 1–5 \| 641 \| 24 \| CC BY-SA 4.0 \| `robot-lev/rh20t_cfg3`, `robot-lev/rh20t_cfg4` \|
	\| RH20T, configurations 3–4 (`rh20t`), scenes 6–10 \| 639 \| 13 \| CC BY-NC 4.0 \| same \|
	\| RoboArena DataDump 2026-07-17 (`roboarena`) \| 1,136 \| 70 \| MIT \| `RoboArena/DataDump_07-17-2026` \|
	\| RoboMIND, failure data (`robomind`) \| 969 \| 81 \| Apache-2.0 \| `x-humanoid-robomind/RoboMIND` \|
	\| VLAC-Cut, ARX data (`vlaccut`) \| 2,516 withheld \| 134 withheld \| no license terms \| `InternRobotics/VLAC-Cut-Benchmark` \|
	\| General replay \| \| \| \| \|
	\| HelpSteer3 (`helpsteer3`) \| 13,267 \| – \| CC BY 4.0 \| `nvidia/HelpSteer3` \|
	\| A-OKVQA (`aokvqa`) \| 4,990 \| – \| see ² \| `HuggingFaceM4/A-OKVQA` \|
	\| Hermes function calling (`agent_tool`) \| 4,502 \| – \| Apache-2.0 \| `NousResearch/hermes-function-calling-v1` \|
	\| ScienceQA (`scienceqa`) \| 3,410 \| – \| CC BY-NC-SA 4.0 ³ \| `derek-thomas/ScienceQA` \|
	\| CommonsenseQA (`commonsense`) \| 2,994 \| – \| MIT \| `tau/commonsense_qa` \|
	\| QASC (`qasc`) \| 2,662 \| – \| CC BY 4.0 \| `allenai/qasc` \|
	\| VideoFeedback (`video_feedback`) \| 500 \| – \| Apache-2.0 \| `TIGER-Lab/VideoFeedback` \|
	\| ARC-Challenge (`arc`) \| 344 \| – \| CC BY-SA 4.0 \| `allenai/ai2_arc` \|

	¹ GRM's images come from AgiBot World, AgileX, DROID, EgoDex, Galaxea, LIBERO, Pika, RoboCasa, RoboTwin and dual-arm Franka
	data; those datasets' own terms may also apply, and some are non-commercial. The upstream repository asks you to accept its
	terms. ² The A-OKVQA repository (`allenai/aokvqa`) is Apache-2.0; its images are COCO 2017 images, whose use follows the Flickr
	terms of use. ³ The original ScienceQA release is CC BY-NC-SA 4.0; the mirror we used lists CC BY-SA 4.0, and so does `_meta.license`
	in those rows. Where `_meta.license` (kept byte-identical to the training set) differs from this table, follow the table.

	Each row's `_meta.source` and `_meta.license` make it easy to drop sources and retrain. Dropping the non-commercial sources
	does not make the rest safe for commercial use: GRM's images come from datasets with their own terms.

	## What is asked

	\| Target type \| Train \| Calibration \| Development \| Question \|
	\|---\|---:\|---:\|---:\|---\|
	\| `robopulse` \| 88,751 \| 281 \| 1,711 \| is the AFTER observation closer to the goal than the BEFORE one, given start and end references (RoboPulse style) \|
	\| `rrb` \| 44,917 \| 392 \| 1,608 \| 1–5 progress score of a rollout video (RoboRewardBench style) \|
	\| `replay` \| 32,669 \| – \| – \| general text, image and tool-use judgement \|
	\| `jj_success` \| 32,433 \| 361 \| 1,246 \| did the step or trajectory succeed \|
	\| `jj_mode_plan` \| 30,933 \| 396 \| 1,455 \| failure mode; is a plan correct; what is wrong with a plan \|
	\| `robopulsepp` \| 26,873 \| 525 \| 1,869 \| did progress rise or fall between consecutive observations, 1–3 camera views (RoboPulse++ style) \|
	\| `jj_pref` \| 19,533 \| 144 \| 696 \| which of two trajectories is better \|
	\| `jj_loc` \| 10,686 \| 180 \| 563 \| at which step or frame the failure happens \|

	`jj_*` follow the task types of JevAny-Robotics-Public. Questions are yes/no (`noul`, 170,202), multiple choice (`choice`, 85,105)
	or 1–5 scores (`score`, 47,417). Labels come from the source's human annotation (`human_expert`, 62,203; `human_multi`, 13,767),
	from how the question was built (`construction`, 167,099: for example time order, or a plan we perturbed), from RoboReward's
	AI-assisted labels (`ai_assisted`, 55,079) or from an AI check of human failure labels (`ai_verified`, 74).

	<details>
	<summary>By family</summary>

	\| Family \| Train \| Calibration \| Development \| Label source \|
	\|---\|---:\|---:\|---:\|---\|
	\| `grm_rp_pair` \| 80,752 \| 199 \| 1,393 \| construction \|
	\| `roboreward_train_progress` \| 44,917 \| 392 \| 1,608 \| ai_assisted, human_expert \|
	\| `densereward_pp_step` \| 19,975 \| 521 \| 971 \| construction \|
	\| `roboreward_train_traj_pref` \| 16,871 \| 128 \| 572 \| ai_assisted, human_expert \|
	\| `replay_helpsteer3` \| 13,267 \| – \| – \| human_multi \|
	\| `guardian_rlbench_exec_success` \| 10,846 \| 100 \| 100 \| construction \|
	\| `densereward_rp_pair` \| 7,999 \| 82 \| 318 \| construction \|
	\| `robovqa_train_exec_success` \| 7,997 \| 99 \| 401 \| human_expert \|
	\| `airoa_rpp_pair` \| 6,778 \| – \| 892 \| human_expert, construction \|
	\| `guardian_bdv2_exec_success` \| 6,276 \| 54 \| 313 \| construction \|
	\| `guardian_rlbench_exec_failure_mode` \| 5,400 \| 38 \| 37 \| construction \|
	\| `replay_aokvqa` \| 4,990 \| – \| – \| human_expert \|
	\| `guardian_rlbench_plan_correct` \| 4,876 \| 76 \| 74 \| construction \|
	\| `replay_agent_tool` \| 4,502 \| – \| – \| human_expert \|
	\| `guardian_bdv2_plan_correct` \| 4,315 \| 48 \| 242 \| construction \|
	\| `robofac_failure_step` \| 3,768 \| – \| 343 \| construction \|
	\| `vifailback_failure_step` \| 3,460 \| 90 \| 110 \| human_expert \|
	\| `vifailback_failure_frame` \| 3,458 \| 90 \| 110 \| human_expert \|
	\| `replay_scienceqa` \| 3,410 \| – \| – \| human_expert \|
	\| `guardian_bdv2_exec_failure_mode` \| 3,195 \| 23 \| 122 \| construction \|
	\| `replay_commonsense` \| 2,994 \| – \| – \| human_expert \|
	\| `replay_qasc` \| 2,662 \| – \| – \| human_expert \|
	\| `guardian_rlbench_plan_failure_mode` \| 2,435 \| 38 \| 37 \| construction \|
	\| `guardian_bdv2_plan_failure_mode` \| 2,175 \| 24 \| 122 \| construction \|
	\| `robocoin_plan_correct` \| 1,871 \| 6 \| 82 \| human_expert, construction \|
	\| `vifailback_failure_type` \| 1,500 \| 137 \| 158 \| human_expert \|
	\| `robofac_failure_type` \| 1,500 \| – \| 300 \| construction \|
	\| `agibot_exec_failure_mode` \| 1,471 \| – \| 156 \| human_expert \|
	\| `robofac_traj_success` \| 1,280 \| 42 \| 158 \| construction \|
	\| `roboarena_traj_pref` \| 1,136 \| 10 \| 60 \| human_expert \|
	\| `vifailback_success` \| 1,084 \| 47 \| 65 \| human_expert \|
	\| `airoa_traj_success` \| 1,042 \| – \| 52 \| human_expert \|
	\| `airoa_exec_success` \| 1,040 \| – \| 52 \| human_expert \|
	\| `robomind_exec_failure_mode` \| 969 \| – \| 81 \| human_expert \|
	\| `robocoin_plan_failure_mode` \| 944 \| 4 \| 40 \| construction \|
	\| `reassemble_traj_success` \| 738 \| 6 \| 18 \| human_expert \|
	\| `reassemble_exec_success` \| 738 \| 5 \| 19 \| human_expert \|
	\| `robocoin_traj_success` \| 526 \| – \| 26 \| human_expert, construction \|
	\| `airoa_traj_pref` \| 521 \| – \| 26 \| human_expert \|
	\| `replay_video_feedback` \| 500 \| – \| – \| human_multi \|
	\| `rh20t_traj_success` \| 382 \| 4 \| 8 \| human_expert \|
	\| `rh20t_exec_success` \| 382 \| 4 \| 8 \| human_expert \|
	\| `reassemble_traj_pref` \| 369 \| 1 \| 11 \| human_expert \|
	\| `replay_arc` \| 344 \| – \| – \| human_expert \|
	\| `rh20t_traj_pref` \| 324 \| 2 \| 5 \| human_expert \|
	\| `robocoin_traj_pref` \| 261 \| 3 \| 9 \| construction \|
	\| `rh20t_exec_failure_mode` \| 192 \| 2 \| 4 \| human_expert \|
	\| `agibot_traj_success` \| 102 \| – \| 26 \| human_expert, construction \|
	\| `vifailback_rpp_pair` \| 64 \| 4 \| 6 \| ai_verified \|
	\| `airoa_plan_correct` \| 60 \| – \| – \| human_expert, construction \|
	\| `agibot_rpp_pair` \| 56 \| – \| – \| human_expert \|
	\| `agibot_traj_pref` \| 51 \| – \| 13 \| construction \|
	\| `airoa_plan_failure_mode` \| 30 \| – \| – \| construction \|

	GRM by dataset family (train): AgiBot 10,941, AgileX 10,941, DROID 10,941, EgoDex 10,941, RoboTwin 10,935, Galaxea 8,000,
	RoboCasa 8,000, LIBERO 4,803, Pika 2,910, dual-arm Franka 2,340.

	</details>

	## Record format

	One JSON object per line, in JevAny's System One format. `state` is what the judge is shown, `questions.decision` the question
	and its label, `media` the attached images or videos, and `_meta` where the record comes from. A RoboRewardBench-style record,
	with the instructions and criteria shortened:

	```json
	{"state": {"task": "Pick fruit", "video": "the rollout (first to last frame)"},
	"questions": {"decision": {"type": "score",
	"instructions": "You are watching one rollout of a robot attempting a manipulation task. ... Rate the robot's end-of-episode progress on the task on a 1-5 scale ...",
	"criteria": ["1 - No success: ...", "2 - Minimal progress: ...", "3 - Partial completion: ...", "4 - Near completion: ...", "5 - Perfect completion: ..."],
	"label": 0, "src": "roboreward_train_progress"}},
	"media": [{"type": "video", "uri": "media/roboreward/99584eef89e6a37539f3839c.mp4"}],
	"_meta": {"id": "roboreward/train/05252", "group_id": "roboreward/berkeley_mvp_converted_externally_to_rlds/pick fruit",
	"source": "roboreward", "origin": "teetone/RoboReward@469b9af76e8539e8d2ac553b081307738fd92ca5",
	"family": "roboreward_train_progress", "label_source": "ai_assisted", "license": "CC-BY-4.0", "split": "train",
	"target_type": "rrb", "extra": {"subset": "berkeley_mvp_converted_externally_to_rlds", "reward": 1, "...": "..."},
	"n_media": 1, "approx_tokens": 192, "...": "..."}}
	```

	- `label` is `true` / `false` for `noul`, the key of the right option in `criteria` for `choice`, and the score minus one for
	`score` (0 = score 1).
	- `_meta.group_id` is the unit of the train / val split (task, scene or session); `_meta.origin` is `repository@revision`.
	- 4,502 tool-use records (`agent_tool`) carry a second question.

	## How it was built

	- Only the train side of each source; no item of any benchmark's test or eval split.
	- Decontamination against RoboPulse, RoboPulse++, RoboRewardBench and JevAny-Robotics-Public: their episodes are blocked by key,
	and every image and video frame (2 fps) is pHash-checked against 168,861 frames of those benchmarks. A match within Hamming
	distance 4, confirmed by a pixel MAD below 6/255, removes the whole episode or session.
	- RoboReward rows follow the official split: they share no episode with the RoboRewardBench test set, but they are not
	frame-disjoint from it (3,771 rows have a frame that matches a test video of the same scene).
	- Calibration and development are held out from train by group and were never trained on.

	Known label caveats. RoboReward clips cut before the end of an episode carry labels that a careful annotator reproduces only
	to within ±1 (20% exactly, 80% within one point). RoboReward's RoboArena-derived scores give credit for attempting. Instruction
	length alone predicts a RoboReward score below 5 (AUROC 0.70 on train, 0.68 on the RoboRewardBench test), because
	counterfactual instructions are longer.

	## Rebuilding the VLAC-Cut rows

	[`InternRobotics/VLAC-Cut-Benchmark`](https://huggingface.co/datasets/InternRobotics/VLAC-Cut-Benchmark) is public, but its
	LICENSE is a placeholder, so we redistribute neither its frames nor its annotations. Its ARX rows (2,516 train, 33 calibration,
	101 development and the 342-row `rpp_threshold_dev.jsonl`) are RoboPulse++-style pairs of consecutive human keyframes,
	including 1,258 real falls in train (363 of them with several views). To rebuild them:

	```bash
	hf download InternRobotics/VLAC-Cut-Benchmark splits/train.json data/train_videos.tar --repo-type dataset \
	--revision fd312e11643bfd7d747a76cad57f0330c4cc0ec0 --local-dir VLAC-Cut-Benchmark # about 90 GB
	pip install av pillow numpy
	python vlaccut_rebuild/rebuild_vlaccut.py --vlac VLAC-Cut-Benchmark --workers 8 # about 16 GB of RAM
	```

	The script reads the instruction, progress values and action description of each keyframe from your copy, decodes the 7,733
	frames, and writes the five full JSONL files with every row at its original line, plus the original `manifest.json` (the
	released files are kept as `.without_vlaccut.`). It then checks each file against the sha256 of the training set
	(`vlaccut_rebuild/original_files.json`). From this folder it uses only the keyframe pairs and views we chose
	(`vlaccut_index.jsonl`) and our English translations of the 688 instructions (`instructions_en.json`). On our machine it
	reproduced every file and every frame byte for byte; other FFmpeg or Pillow builds may encode some frames slightly differently,
	which changes no row.

	## Verification

	Run on this folder before upload (`verify.json`):

	- JevAny `load_split` passes for train, calibration and development; every source is declared in `manifest.json`.
	- Every line equals the training-set line, in order, with only the VLAC-Cut lines removed.
	- No duplicate ids; no group shared between train and val.
	- The 704,700 referenced media files and the files in the 21 shards are the same set, with matching sizes and shard sha256.

	## Acknowledgements

	This dataset exists because the authors of the datasets above released their data. Please cite them when you use the
	corresponding parts. The model trained on it, its evaluation and its full acknowledgements are on the
	[model card](https://huggingface.co/SimpleJev/JevAny-Robotics-4B-LoRA).