---
language:
- en
task_categories:
- robotics
tags:
- embodied-ai
- visual-robustness
- signal-intervention
size_categories:
- 10K<n<100K
pretty_name: Embodied Reference and Signal Collection
---

# Embodied Reference and Signal Collection

A research prototype containing **1,000 selected episodes**, **3,628 reference images**,
and **90,700 single-factor signal interventions**. Images and metadata total approximately 9.92 GB.

| Source | Episodes | Reference images | Intervention images |
|---|---:|---:|---:|
| LIBERO-R | 82 | 267 | 6,675 |
| RoboInter-Data / DROID | 918 | 3,361 | 84,025 |
| Total | 1,000 | 3,628 | 90,700 |

## Files and loading

```text
reference_collection/
  references.json
  images/{dataset}/{episode}/progress_XX.png
signal_collection/
  signals.json
  images/{dataset}/{episode}/progress_XX/{factor}_{severity}.png
```

Images are distributed in ZIP shards under `archives/` to avoid uploading 94,328 individual image files. `archives.json` lists image counts, sizes and SHA-256 checksums. ZIP storage preserves the PNG bytes exactly. The tree above is the layout AFTER extraction. Download as a file-based dataset. Each JSON's image paths are relative to that JSON's
directory. Keep both top-level collection folders together; signals.json references
the original reference images using ../reference_collection/ paths.

```python
import json
import zipfile
from pathlib import Path
from huggingface_hub import snapshot_download
from PIL import Image

root = Path(snapshot_download(repo_id="xiaojiahao/embodied-visual-intervention", repo_type="dataset"))
for archive in sorted((root / "archives").glob("*.zip")):
    with zipfile.ZipFile(archive) as z:
        z.extractall(root)

references = json.loads((root / "reference_collection/references.json").read_text(encoding="utf-8"))
signals = json.loads((root / "signal_collection/signals.json").read_text(encoding="utf-8"))
sample = signals["references"][0]
original = Image.open(root / "signal_collection" / sample["reference_image"])
variant = Image.open(root / "signal_collection" / sample["variants"][0]["image"])
```

## Reference selection and annotations

One episode per normalized instruction (case, whitespace and final punctuation).
This is lexical deduplication, not a guarantee of semantically distinct tasks.
All 82 LIBERO-R instruction strings are covered through libero-100-r; its 83 task IDs
include a repeated instruction. RoboInter uses DROID episodes in index order,
requires Primary instruction/subtask annotation quality, rejects empty instructions,
and also deduplicates annotated instructions and source trajectories across cameras.

LIBERO references use the last frame of each distinct reasoning-update state
(end-exclusive interpretation). RoboInter uses the first frame of each contiguous
subtask plus the final observation. Episode metadata includes instruction, source
revision, frame/timestamp information, subtasks, automatically extracted noun phrases,
and original state/action chunks (up to 8 steps, truncated at episode end).

LIBERO images are 256x256; RoboInter images are 320x180 from the processed source videos.
Original high-resolution video trials are NOT included. Original camera orientations
and pixel dimensions are retained. Source frame indices are distinguished from
episode-local indices where necessary.

## Seven signal factors

Each factor is applied independently to the original reference. There are 25 nonzero
variants per reference. Severity 0 reuses the original image without a duplicate file.

| Factor | Nonzero severities | Parameters in order |
|---|---|---|
| luminance | -2,-1,+1,+2 | linear exposure gains 0.35,0.65,1.3,1.6 |
| contrast | -2,-1,+1,+2 | mean-centered encoded-RGB gains 0.35,0.65,1.2,1.45 |
| chromatic_information | -2,-1,+1,+2 | linear-RGB chroma scales 0,0.5,1.3,1.6 |
| spatial_frequency | -2,-1,+1,+2 | Gaussian blur sigma 3/1.3 px; unsharp sigma 1, amount 0.25/0.5 |
| spatial_sampling | -3,-2,-1 | downsample to 1/8,1/4,1/2 then restore original dimensions |
| snr | -3,-2,-1 | additive RGB Gaussian noise sigma 25,12,5 in 8-bit units |
| amplitude_capacity | -3,-2,-1 | uniform 2,4,6-bit/channel quantization stored in RGB PNG |

signals.json records reference IDs, paths, image hashes, actual transform parameters,
noise seeds, implementation hashes and library versions. Noise strengths share a
standard-normal field within a reference; seeds differ across references.

## Limitations

- This is a reference/intervention collection, not a fully adjudicated benchmark or train/test split.
- RoboInter task_complete is null: episode termination is not treated as verified success.
  Intermediate progress assumes preceding annotated subtasks were completed; final progress is unknown.
- Automatically extracted perception noun phrases require review. No bounding-box GT is supplied.
- Original task/action annotations remain linked to the scene, but interventions can obscure evidence;
  image-only task solvability and label visibility are not guaranteed.
- Equal pixel-based blur parameters need not imply equal difficulty across source resolutions.
- No model evaluation results, downloaded video caches, credentials or model checkpoints are included.

## Sources, attribution and terms

This collection redistributes selected source images/annotations and adds image sampling,
metadata organization, lexical selection and deterministic signal transformations.
Original data and annotation authors retain their rights. This card does not relicense
third-party source material or grant rights beyond its upstream terms.

- [NVIDIA LIBERO-R](https://huggingface.co/datasets/nvidia/libero-r-datasets), revision
  `61ac0be5c084b61e60d50c21c97052b58a65a31b`, based on [LIBERO](https://libero-project.github.io/).
  Reasoning annotation work: Yilin Wu, Anqi Li, Tucker Hermans, Fabio Ramos, Andrea Bajcsy,
  Claudia Perez-D'Arpino, *Do What You Say: Steering Vision-Language-Action Models via Runtime
  Reasoning-Action Alignment Verification* (2025), https://arxiv.org/abs/2510.16281.
  The inspected pinned dataset README does not state a dataset license; consult the source
  maintainers and underlying dataset terms for intended reuse.
- [InternRobotics RoboInter-Data](https://huggingface.co/datasets/InternRobotics/RoboInter-Data),
  revision `0208b79c34eca4d214cdb5d07f5ae0f7cc634340`, based on [DROID](https://droid-dataset.github.io/).
  RoboInter's source notice specifies [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).
  Preserve attribution, noncommercial and share-alike obligations for applicable derived material,
  together with underlying DROID terms. See the [RoboInter project](https://github.com/InternRobotics/RoboInter)
  for the authors' citations and documentation.

## Where and What intervention release

This release adds the frozen Where collection and the available **partial What (9/11)** collection. The original reference and signal data, licensing and source attribution above remain unchanged.

| Collection | Reference frames | Intervention images | Status |
|---|---:|---:|---|
| Where | 3,628 | 174,848 | Generated; 87,424 regions, each with removal and retain |
| What | 3,628 | 32,652 | Partial: nine methods; state and identity not generated |

Where includes 87,424 binary masks, 87,424 feathered alpha masks and 3,628 copied reference PNGs, in addition to removal/retain images (353,324 PNGs total). Not every concept has a valid region on every reference; the manifest records actual available regions.

What methods available on all references: color, texture, shading, size, shape, material, parts, text_symbol and flat_2d. Quality scores exist for 6,456 images only; images are **not filtered** by quality score. Missing scores are not passes. Local judge reports and caches are excluded. State and identity must not be reported as evaluated in this snapshot.

Metadata: `where_collection_final/where.json`, `what_collection/what.json`, and each collection's `methods.json`. Paths are relative to the corresponding manifest. `intervention_archives.json` records ZIP sizes, SHA-256 checksums, file counts and collection coverage. Extract selected ZIPs at the dataset root, preserving their top-level folder. Existing `archives.json` continues to describe only the original reference/signal release.

Download and evaluate with the [Where/What evaluation instructions](https://github.com/Visual-and-Spatial-AI-Lab/cvpr2027/blob/master/INTERVENTION_EVALUATION.md). Cognition uses paired A/B orders; planning uses all cyclic option rotations. Source labels can become visually unanswerable after an intervention and are not new human annotations of edited images.

## Evaluation correction: What means Where × What

The `what_collection` PNGs in this dataset are **full-frame source assets**, not the final What evaluation inputs. Official What evaluation combines each source What image with eligible Where regions **once, replacing only the selected region**, using the unchanged saved feathered alpha. Pixels outside the region are preserved, apart from the existing feathered boundary. For uint8 alpha `w`, composition is `(reference*(255-w) + what*w + 127)//255`. There is no complementary outside-region What variant. Only the standalone Where suite keeps both removal and retain modes.

The current assets support **786,816 Where × What composites** (nine available What methods × 87,424 Where regions, with one inside-region replacement each). These composites are generated and cached locally on demand, not included as a second set of precomputed ZIP archives. State and identity remain unavailable. The current **v5 selection** intersects measured non-negligible pixel change with the frozen region/attribute eligibility policy, retaining **457,549 composites**. Category eligibility alone retains 465,464; 7,915 of those fail the pixel-change rule. The pixel rule excludes exact equality or both mean absolute RGB difference below 0.25 uint8 levels and fewer than 0.1% of pixels with maximum-channel difference at least 8. The policy is an experimental scope, not a quality certification; no GT or model outputs enter selection. Source assets are unchanged. Qwen and PaliGemma now use fixed **batch32**, compact per-sample probabilities/raw outputs and per-batch durable writes. Both cognition and planning are queued, reference excluded; old single-item results are kept separately because batch inference can change numerical probabilities. See the linked instructions for preparation, exact eligibility matrix, resume and output paths.

Use [the unified cognition/planning runner](https://github.com/Visual-and-Spatial-AI-Lab/cvpr2027/blob/master/INTERVENTION_EVALUATION.md): one command loads a selected model once, evaluates reference, Where and Where × What, and saves three separate result directories. Reference is evaluated once. Old full-frame-only What results are diagnostic and must not be presented as Where × What evaluation.