Datasets:

Raynh945
/

ICBench

pretty_name: ICBench
license: cc-by-4.0
language:
  - en
size_categories:
  - n<1K
tags:
  - robotics
  - vision-language-action
  - linguistic-grounding
  - libero
  - benchmark
configs:
  - config_name: default
    data_files:
      - split: test
        path: icbench.jsonl

ICBench

ICBench is a controlled diagnostic benchmark for evaluating linguistic grounding in Vision-Language-Action (VLA) models. It pairs original LIBERO task instructions with contradictory instructions while keeping the visual scene unchanged.

This release accompanies Restoring Linguistic Grounding in VLA Models via Train-Free Attention Recalibration (IGAR).

Project page: https://ray-nh.github.io/igar/
Data file: icbench.jsonl
Format: UTF-8 JSON Lines; one instruction pair per row
Release contents: instruction text, contradiction labels, and references to LIBERO BDDL task paths. Robot trajectories, images, simulator assets, and model weights are not included.

Dataset composition

The file contains 120 instruction pairs across 30 distinct tasks. Each task has four contradictory instruction variants.

LIBERO suite	Distinct tasks	Instruction pairs
`libero_spatial`	10	40
`libero_object`	10	40
`libero_goal`	10	40
Total	30	120

Variant	Contradiction	Pairs
`V1`	Manipulated object attribute	30
`V2`	Target attribute	30
`V3`	Both manipulated object and target attributes	30
`V4`	Spatial relation	30

All records are exposed as a single test split for evaluation. The source file does not define a training or validation partition.

Data fields

Field	Type	Description
`bddl_path`	string	Relative path to the corresponding LIBERO BDDL task specification.
`suite`	string	LIBERO task suite.
`original_instruction`	string	Original task instruction.
`contradiction.instruction`	string	Contradictory instruction used with the same scene.
`contradiction.variant`	string	Contradiction category: `V1`, `V2`, `V3`, or `V4`.

Example record (unchanged from the source file):

{
  "bddl_path": "libero_spatial/pick_up_the_black_bowl_from_table_center_and_place_it_on_the_plate.bddl",
  "contradiction": {
    "instruction": "Pick the akita white bowl from table center and place it on the plate",
    "variant": "V1"
  },
  "original_instruction": "Pick the akita black bowl from table center and place it on the plate",
  "suite": "libero_spatial"
}

Loading

Install the Hugging Face Datasets library:

pip install datasets

from datasets import load_dataset

dataset = load_dataset("Raynh945/ICBench", split="test")
print(len(dataset))  # 120
print(dataset[0]["contradiction"]["instruction"])

The JSONL file can also be read without third-party libraries:

import json

with open("icbench.jsonl", encoding="utf-8") as f:
    records = [json.loads(line) for line in f if line.strip()]

License

The ICBench instruction dataset is released under CC BY 4.0. Please credit the authors and cite the paper when using this release. The full license text is in LICENSE. LIBERO-derived material retains the upstream MIT notice in NOTICE.

Citation

@inproceedings{zhang2026restoring,
  title={Restoring Linguistic Grounding in VLA Models via Train-Free Attention Recalibration},
  author={Ninghao Zhang and Bin Zhu and Shijie Zhou and Jingjing Chen},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2026},
  url={https://arxiv.org/abs/2603.06001}
}