---
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}
}
```