| --- |
| 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](https://arxiv.org/abs/2603.06001)** (IGAR). |
|
|
| - **Project page:** https://ray-nh.github.io/igar/ |
| - **Data file:** [`icbench.jsonl`](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): |
|
|
| ```json |
| { |
| "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: |
|
|
| ```bash |
| pip install datasets |
| ``` |
|
|
| ```python |
| 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: |
|
|
| ```python |
| 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](https://creativecommons.org/licenses/by/4.0/). Please credit the authors and cite the paper when using this release. The full license text is in [`LICENSE`](LICENSE). LIBERO-derived material retains the upstream MIT notice in [`NOTICE`](NOTICE). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
