Datasets:

IRVLUTD
/

RPX

license: cc-by-4.0
pretty_name: 'RPX: Robot Perception X'
task_categories:
  - image-segmentation
  - depth-estimation
  - object-detection
  - visual-question-answering
language:
  - en
tags:
  - robotics
  - embodied-ai
  - rgb-d
  - benchmark
  - perception
  - manipulation
  - stereo
  - tabular
  - image
  - video
size_categories:
  - 100B<n<1T
configs:
  - config_name: multi_object
    description: MOS phase records, tertile-cut by Effort-Stratified Difficulty.
    default: true
    data_files:
      - split: easy
        path: splits/easy.parquet
      - split: medium
        path: splits/medium.parquet
      - split: hard
        path: splits/hard.parquet
  - config_name: media_preview
    description: >-
      Data Studio media table with one video-layout RGB/depth/mask image preview
      per MOS phase.
    data_files:
      - split: preview
        path: preview/media_preview.parquet
  - config_name: single_object
    description: >-
      SOS selected-object catalog; one 360 collection per object, no difficulty
      split.
    data_files:
      - split: objects
        path: manifest/selected_sos_objects_v1.parquet

RPX: Robot Perception X

A real-world RGB-D benchmark for evaluating robot perception under embodied deployment conditions.

Code: https://github.com/IRVLUTD/RPX

Watch the RPX teaser video

Dataset at a glance


Multi-object scenes (MOS)	100 (`scene001` to `scene100`, 3 phases each: clutter / interaction / clean)
Single-object scenes (SOS)	70 selected objects (one 360 collection per object)
Frame manifest rows	110,000 (75,000 MOS + 35,000 selected SOS)
MOS mask-object rows	2,100 local mask IDs mapped to global object IDs

Scene renames are documented in manifest/scene_name_mapping_v1.csv, mapping original scene names to scene001 through scene100.

The Hugging Face Dataset Viewer exposes multi_object as structured split tables and includes a media_preview config with an actual image media column: one video-layout RGB, depth, and mask preview still for each MOS phase.

Effort-Stratified Difficulty (ESD)

ESD is the difficulty protocol used to split multi-object scenes. Each sceneXXX.phaseY receives an RPX Difficulty Score (rpx_ds); higher scores indicate harder perception conditions. The score is produced by effort_stratified_v1 using primary_method: mean_pn, a weighted aggregate of the normalized feature set recorded in splits/scene_splits.json.

The released split files expose two levels:

level	where	construction
phase-level splits	`splits/easy.txt`, `medium.txt`, `hard.txt` and CSV copies	all 300 scene phases are sorted by `rpx_ds` and tertile-cut into 100 easy, 100 medium, 100 hard entries
scene-level tiers	`splits/scene_splits.json`	each scene score is the mean of its three phase scores, then 100 scenes are tertile-cut into 33 easy, 33 medium, 34 hard scenes

Because scene tiers are aggregated, a hard scene can still contain an easy phase. Use the phase-level split when downloading or evaluating MOS tasks.

The ESD feature weights are fully accounted for in splits/scene_splits.json: 27 feature names, 27 weights, no missing weights, and total weight 1.0. iter_mean and iter_max each carry weight 0.125; the other 25 features each carry weight 0.03.

feature group	features
refinement effort	`iter_mean`, `iter_max`
object complexity	`obj_mean`, `obj_std`, `obj_consist`
occlusion	`occ_mean`, `occ_p90`, `occ_heavy`
depth quality	`depth_invalid`, `depth_invalid_mask`, `depth_std`, `depth_std_mask`
image appearance	`specular`, `dark`
mask/visibility stability	`area_cv`, `area_drop`, `vis_instability`
motion and trajectory	`trans_mean`, `trans_p90`, `rot_mean`, `rot_p90`, `jerk`
fisheye quality	`fisheye_dark`, `fisheye_bright`, `fisheye_sharpness`, `fisheye_corr`, `fisheye_texture`

The split tables include the final rpx_ds, difficulty, scene_tier, and scene_score fields for Dataset Viewer filtering. Raw per-feature values are not duplicated in those tables; splits/scene_splits.json is the source of truth for the score/tier assignments and the feature/weight provenance.

Modality inventory

The table below describes this cleaned release. cam_pose_icp is not included in this cleaned release. Use manifest/frames_v1.parquet and the identity manifests under manifest/ as the source of truth.

modality	files	bytes
`cam_pose`	110,000	163.3 MB
`depth`	110,000	16.9 GB
`fisheye`	220,000	63.4 GB
`rgb`	110,000	41.0 GB
`masks`	110,000	341.7 MB

Quick start

pip install "rpx-benchmark[hub]"
hf auth login

from rpx_benchmark.dataset_hub import download_for_task

# Pull just RGB + masks for the Easy difficulty tier — never the whole repo.
res = download_for_task(task="segmentation", split="easy",
                          repo_id="IRVLUTD/RPX")
print(res.local_dir, res.matched_scenes)

# Or from the CLI:
python -m rpx_benchmark.dataset_hub.cli download \
    --task segmentation --split easy \
    --repo-id IRVLUTD/RPX

A subsequent call for a different task on the same split (e.g. relative_pose) reuses the cached RGB tars and only fetches the new modality (cam_pose) as the delta.

Repo layout

IRVLUTD/RPX/
├── manifest/
│   ├── frames_v1.parquet                 # per-frame metadata
│   ├── scene_name_mapping_v1.csv         # original scene names to scene001..scene100
│   ├── selected_sos_objects_v1.csv       # selected 70-object SOS catalog
│   ├── selected_sos_objects_v1.parquet   # Dataset Viewer SOS catalog table
│   ├── object_catalog_v1.json            # SOS object/global-ID catalog
│   ├── mos_raw_mask_object_map_v1.csv    # MOS local mask IDs from sam2 metadata
│   ├── mos_mask_object_map_v1.csv        # MOS local mask IDs joined to SOS global IDs
│   ├── mos_mask_object_map_v1.parquet    # parquet copy of the MOS map
│   └── current.json                      # default version per label modality
├── splits/
│   ├── scene_splits.json
│   ├── easy.txt  medium.txt  hard.txt      # phase-level split IDs
│   ├── easy.csv  medium.csv  hard.csv      # human-readable split tables
│   └── easy.parquet  medium.parquet  hard.parquet  # Dataset Viewer split tables
├── preview/
│   ├── media_preview.parquet               # Dataset Viewer image media table
│   ├── data_studio_preview.csv             # preview index with source shard links
│   └── image_examples/preview/             # source JPEGs for the media preview
├── scenes/<scene_id>/<phase>/                     # MOS
│   ├── rgb.tar  depth.tar  fisheye.tar
│   └── labels/{cam_pose,masks,masks_aux,sam2_meta}/v1.tar
├── objects/<object_id>/0/                         # SOS
│   └── (same modality structure)
├── objects_meta/                                  # questionnaire dedup
│   ├── _index.json
│   └── <object_id>/questionnaire.json
└── README.md   ←  this file

Object identity manifests

SOS objects use dataset-wide IDs from manifest/selected_sos_objects_v1.csv and manifest/object_catalog_v1.json.

field	meaning
`global_object_id`	New integer ID, 1 to 70
`source_catalog_id`	Original catalog/PDF ID, kept as a string such as `88.2`
`object_id`	Actual folder name, such as `mug.2`
`questionnaire_path`	Linked questionnaire under `objects_meta/<object_id>/questionnaire.json`

MOS masks use local IDs. A local_mask_id is only meaningful within one scene_id + phase; it is not a global object ID. The raw source is scenes/<scene_id>/<phase_index>/labels/sam2_meta/v1.tar:sam2/mask_to_object.json.

Use manifest/mos_mask_object_map_v1.csv or manifest/mos_mask_object_map_v1.parquet to join MOS masks to SOS objects:

scene_id,phase,local_mask_id,object_id,global_object_id,source_catalog_id,object_name
scene001,phase0,2,boot.2,11,18.2,boot

That row means scene001/0 mask ID 2 is boot.2, whose global object ID is 11, with questionnaire objects_meta/boot.2/questionnaire.json and SOS template objects/boot.2/.

Tasks

Multi-object (use a difficulty split)

recipe	inputs → labels
`monocular_depth`	['rgb'] → ['depth']
`rgbd_segmentation`	['depth', 'rgb'] → ['masks']
`segmentation`	['rgb'] → ['masks']
`relative_pose`	['rgb'] → ['cam_pose']
`rgbd_relative_pose`	['depth', 'rgb'] → ['cam_pose']
`stereo_depth`	['fisheye'] → ['depth']
`object_tracking`	['rgb'] → ['masks']
`vqa`	['rgb'] → ['questionnaire', 'vqa']

Single-object (no split — these are object templates)

recipe	inputs → labels
`object_templates`	['rgb'] → ['masks']
`object_templates_rgbd`	['depth', 'rgb'] → ['masks']
`object_pose_library`	['depth', 'rgb'] → ['cam_pose', 'masks']

Label versioning

Labels live at labels/<name>/v<N>.tar. Newer versions land at new paths; old versions stay reachable for reproducibility.

modality	current version
`masks`	`v1`
`masks_aux`	`v1`
`sam2_meta`	`v1`
`cam_pose`	`v1`

To pin to a specific version:

download_for_task(
    task="relative_pose", split="easy", repo_id="itaykadosh/RPX",
    label_versions={"cam_pose": "v1"},   # don't auto-upgrade to v2
)

Citation

@misc{rpx2026,
    title  = {RPX: Robot Perception X — A real-world RGB-D benchmark for
              embodied perception},
    author = {IRVL UT Dallas},
    year   = 2026,
    url    = {https://huggingface.co/datasets/itaykadosh/RPX},
}

License

Released under the cc-by-4.0 license.