README.md

metadata

license: apache-2.0
language:
  - en
pretty_name: EMBGuardTest v3
size_categories:
  - n<1K
task_categories:
  - image-text-to-text
tags:
  - embodied-ai
  - safety
  - guardrail
  - physical-hazard
  - benchmark
configs:
  - config_name: default
    data_files:
      - split: causal_risky
        path: data/causal_risky-*
      - split: decoupled_benign
        path: data/decoupled_benign-*
      - split: selective_risky
        path: data/selective_risky-*
      - split: absent_benign
        path: data/absent_benign-*
dataset_info:
  features:
    - name: id
      dtype: string
    - name: image
      dtype: image
    - name: image_path
      dtype: string
    - name: risk
      dtype: string
    - name: type
      dtype: string
    - name: scenario_type
      dtype: string
    - name: risk_type
      dtype: string
    - name: risk_subtype
      dtype: string
    - name: situation
      dtype: string
    - name: hazard
      dtype: string
    - name: action
      dtype: string
    - name: room
      dtype: string
    - name: multi_scenario
      dtype: string
    - name: pair_item_id
      dtype: string
  splits:
    - name: causal_risky
      num_bytes: 264499562
      num_examples: 75
    - name: decoupled_benign
      num_bytes: 264500374
      num_examples: 75
    - name: selective_risky
      num_bytes: 401910525
      num_examples: 104
    - name: absent_benign
      num_bytes: 263962112
      num_examples: 75
  download_size: 1194900005
  dataset_size: 1194872573

EMBGuardTest v3

EMBGuardTest is the evaluation set of EMBGuard ("EMBGuard: Constructing Hazard-Aware Guardrails for Safe Planning in Embodied Agents", arXiv:2605.30924). Each example is a real-world indoor photograph plus a proposed robot action. A guardrail model must decide whether executing the action in that scene is safe or unsafe.

v3 supersedes EMBGuard/EMBGuardTest_v2: some images and ids in v2 were wrong (mismatched or duplicated images, swapped selective pairs, inconsistent ids), and the id scheme changed. Results on v2 and v3 are not directly comparable.

Splits

split	rows	label (risk / type)	meaning
causal_risky	75	O / unsafe	A hazard is present and causally relevant to the action.
decoupled_benign	75	X / safe	Same image as causal_risky; a hazard-like element is present but decoupled from the action, so the scene should be judged safe.
selective_risky	104	O / unsafe	Several hazard candidates are present; the model must identify the action-relevant risky condition. 52 images, each used by exactly 2 rows.
absent_benign	75	X / safe	The hazard is absent from the scene (same scenario number as causal_risky).

The 75 scenarios in causal_risky, decoupled_benign and absent_benign share numbers: causal_risky-022, decoupled_benign-022 and absent_benign-022 are the same scenario. decoupled_benign re-uses the causal_risky image.

Columns

column	type	description
id	string	{split}-{NNN}, e.g. causal_risky-022. Unique within a split; not meaningful for inference.
image	image	JPEG photograph (EXIF orientation applied; resolution varies, long side up to 5712 px).
image_path	string	Relative path used while building the dataset (informational; files are embedded in the parquet).
risk	string	O (unsafe) or X (safe).
type	string	unsafe or safe.
scenario_type	string	One of the four split names.
risk_type	string	One of 7 hazard categories: Electrical_Risk, Fire_Risk, Crush_Pinch_Risk, Cut_Sharp_Risk, Contamination_Infection_Risk, Slip_Trip_Fall_Risk, Chemical_Toxic_Exposure_Risk.
risk_subtype	string	Finer hazard category (23 values in the causal scenarios, 19 in selective_risky).
situation	string	Unused, always empty (kept for schema compatibility with v2).
hazard	string	Short description of the hazard in the scene. For absent_benign it describes the hazard-free scene.
action	string	The proposed robot action to be judged.
room	string	Room type of the scene: Kitchen, Livingroom, Bedroom, Bathroom, Office or Hallway. Filled for every row. The dataset authors labelled the 48 non-augmented selective_risky rows; the other rows were labelled afterwards by manual review of the photographs.
multi_scenario	string	"yes" for the 56 selective_risky rows whose image contains two scenarios, "no" for all other rows.
pair_item_id	string	selective_risky only: id of the partner row that shares the same image (selective_risky-000 <-> selective_risky-001). Empty in other splits.

Usage

from datasets import load_dataset

ds = load_dataset("EMBGuard/EMBGuardTest_v3")
ex = ds["causal_risky"][0]
print(ex["id"], ex["action"], ex["hazard"])
ex["image"]  # PIL.Image

Evaluation scripts and the guardrail models are in the EMBGuard GitHub repository.

Notes and limitations

• Small by design (329 rows): intended for evaluation only, not for training.
• 27 images have a short side below 1000 px (down to 360 px).
• selective_risky rows are only meaningful together with their partner row (same image, different hazard/action).

Citation

@article{choi2026embguard,
  title   = {EMBGuard: Constructing Hazard-Aware Guardrails for Safe Planning in Embodied Agents},
  author  = {Choi, Dongwook and Kwon, Taeyoon and Jeong, Bogyung and Kim, Minju and Hwang, Yeonjun and Kim, Hyojun and Kim, Byungchul and Jang, Young Kyun and Yeo, Jinyoung},
  journal = {arXiv preprint arXiv:2605.30924},
  year    = {2026},
  url     = {https://arxiv.org/abs/2605.30924}
}