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FBK-TeV/UnoBench

UnoBench UnoBench is a benchmark for target-centric obstruction reasoning in robotic grasping under cluttered scenes. Given a target object, a method must identify the objects that block or constrain access to that target before grasping. UnoBench is built upon MetaGraspNetV2 and extends the initial idea of FreeGraspData. Resources Resource Link Description UnoGrasp code GitHub main branch Method code, checkpoints, inference, and evaluation. Challenge… See the full description on the dataset page: https://huggingface.co/datasets/FBK-TeV/UnoBench.

sourceHugging Facecc-by-nc-4.0updated 2mo agoView on Hugging Face

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UnoBench

UnoBench is a benchmark for target-centric obstruction reasoning in robotic grasping under cluttered scenes. Given a target object, a method must identify the objects that block or constrain access to that target before grasping.

UnoBench is built upon MetaGraspNetV2 and extends the initial idea of FreeGraspData.

Resources

Resource	Link	Description
UnoGrasp code	GitHub main branch	Method code, checkpoints, inference, and evaluation.
Challenge starter kit	GitHub challenge branch	Minimal examples and local evaluators for the UnoBench Challenge.
Project page	tev-fbk.github.io/UnoGrasp	Paper, video, and release links.

Dataset Overview

UnoBench provides synthetic cluttered-scene data with RGB images, Set-of-Mark images, instance annotations, natural-language object descriptions, and obstruction metadata.

The benchmark supports two settings:

Setting	Target input	Expected output	Main use case
NLP	Natural-language target description and RGB image	Obstructing objects, represented by points or grounded object IDs	Vision-language and language-conditioned methods.
SoM	Set-of-Mark image and target object ID	Obstructing object IDs	Object-centric, graph-based, or modular robotic reasoning methods.

Dataset Structure

text

UnoBench/
`-- UnoBenchSyn/
    |-- images.zip
    |-- images_som.zip
    |-- annotations.zip
    |-- test_GT_small.json
    |-- test_nlp_small.jsonl
    |-- test_som_small.jsonl
    |-- challenge_only/
    |   |-- test_nlp.jsonl
    |   `-- test_som.jsonl
    `-- meta_data/
        |-- Synthetic_train.json
        |-- image_id_scene_view_id_mapping.json
        |-- name_for_all.json
        |-- annotations_meta.zip
        `-- occ_info/
            |-- obs_information.json
            `-- masks.zip

After extracting the main archives, the dataset also contains:

text

UnoBenchSyn/
|-- images/        # RGB images
|-- images_som/    # Set-of-Mark images
`-- annotations/   # Instance masks used by NLP point evaluation

File Description

Main Archives

File	Description
`images.zip`	RGB images of synthetic cluttered scenes.
`images_som.zip`	Set-of-Mark images with object IDs / visual prompts.
`annotations.zip`	Instance segmentation masks associated with each image. These masks map image points to object IDs.

Reproduction Files

These files are used by the UnoGrasp code for inference and evaluation on the released small split.

File	Description
`test_GT_small.json`	Ground-truth obstruction annotations for the small test split.
`test_som_small.jsonl`	Evaluation samples for the SoM setting.
`test_nlp_small.jsonl`	Evaluation samples for the NLP setting.

Challenge Files

These files are used by the UnoBench Challenge.

File	Description
`challenge_only/test_som.jsonl`	Challenge Track 1: Set-of-Mark reasoning.
`challenge_only/test_nlp.jsonl`	Challenge Track 2: natural-language reasoning.

Metadata

File	Description
`meta_data/Synthetic_train.json`	Query object, target objects, occlusion paths, and difficulty level for each sample.
`meta_data/image_id_scene_view_id_mapping.json`	Mapping between image IDs, scene IDs, and view IDs.
`meta_data/name_for_all.json`	Human-annotated object descriptions.
`meta_data/annotations_meta.zip`	MetaGraspNetV2 annotations, including depth, semantic segmentation, instance segmentation, and occlusion masks.
`meta_data/occ_info/obs_information.json`	Pairwise obstruction/occlusion information, such as obstruction ratio, contact point, and obstruction degree.
`meta_data/occ_info/masks.zip`	Instance masks for obstruction pairs.

Download

Install the Hugging Face CLI if needed:

bash

pip install -U huggingface_hub

Download the full dataset:

bash

hf download FBK-TeV/UnoBench \
  --repo-type dataset \
  --local-dir ./UnoBench/UnoBenchSyn

Or download individual archives:

bash

hf download FBK-TeV/UnoBench images.zip \
  --repo-type dataset \
  --local-dir ./UnoBench/UnoBenchSyn

hf download FBK-TeV/UnoBench images_som.zip \
  --repo-type dataset \
  --local-dir ./UnoBench/UnoBenchSyn

hf download FBK-TeV/UnoBench annotations.zip \
  --repo-type dataset \
  --local-dir ./UnoBench/UnoBenchSyn

Extraction

After downloading, unzip the main archives:

bash

cd UnoBench/UnoBenchSyn
unzip images.zip
unzip images_som.zip
unzip annotations.zip

Evaluation Splits

Split / file	Purpose
`test_som_small.jsonl`	SoM reproduction with the released UnoGrasp small checkpoint.
`test_nlp_small.jsonl`	NLP reproduction with the released UnoGrasp small checkpoint.
`test_GT_small.json`	Ground truth for local reproduction evaluation.
`challenge_only/test_som.jsonl`	Official challenge queries for the SoM track.
`challenge_only/test_nlp.jsonl`	Official challenge queries for the NLP track.

The challenge test ground truth is reserved for official leaderboard evaluation.

Metadata Format

Metadata files provide scene-level and object-level information, including:

text

image_id
scene_id
view_id
query_object
target_object
occlusion_paths
difficulty
num_paths
k_min
som_only

The obstruction information is target-centric: for each target object, UnoBench describes the objects that obstruct it and the corresponding obstruction paths.

Notes

UnoBench focuses on high-level obstruction reasoning before grasping, rather than low-level grasp pose execution or robot control. In this release, obstruction is operationalized mainly through occlusion relationships in cluttered scenes.

Citation

If you use UnoBench in your research, please cite:

bibtex

@inproceedings{jiao2026obstruction,
  title = {Obstruction Reasoning for Robotic Grasping},
  author = {Runyu Jiao and Matteo Bortolon and Francesco Giuliari and Alice Fasoli and Sergio Povoli and Guofeng Mei and Yiming Wang and Fabio Poiesi},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026}
}

License

UnoBench is released under the CC BY-NC 4.0 license for academic, non-commercial use. Please refer to the license information on the Hugging Face dataset page before using the data.

Contact

For questions about the dataset, please contact:

text

Runyu Jiao: rjiao@fbk.eu
Fondazione Bruno Kessler / University of Trento