# Datacard
This dataset is the evaluation VLM dataset used in VLABench. It is designed to evaluate the planning capabilities of Vision-Language Models (VLMs) in embodied scenarios. 

## Source
- Project Page: [https://vlabench.github.io/](https://vlabench.github.io/)
- Arxiv Paper: [https://arxiv.org/abs/2412.18194](https://arxiv.org/abs/2412.18194)
- Code: [https://github.com/OpenMOSS/VLABench](https://github.com/OpenMOSS/VLABench)

## Uses

The dataset structure is as follows:
```
vlm_evaluation_v1.0/
├── CommenSence/
    ├── add_condiment_common_sense/
    ├── insert_flower_common_sense/
    ├── select_billiards_common_sense/
    ├── select_chemistry_tube_common_sense/
    ├── select_drink_common_sense/
    ├── select_fruit_common_sense/
    ├── select_nth_largest_poker/
    └── select_toy_common_sense/
├── Complex/
    ├── book_rearrange/
    ├── cook_dishes/
    ├── hammer_nail_and_hang_picture/
    ├── take_chemistry_experiment/
    └── texas_holdem/
├── M&T/
    ├── add_condiment/
    ├── insert_flower/
    ├── select_billiards/
    ├── select_book/
    ├── select_chemistry_tube/
    ├── select_drink/
    ├── select_fruit/
    ├── select_poker/
    └── select_toy/
├── PhysicsLaw/
    ├── density_qa/
    ├── friction_qa/
    ├── magnetism_qa/
    ├── reflection_qa/
    ├── speed_of_sound_qa/
    └── thermal_expansion_qa/
├── Semantic/
    ├── add_condiment_semantic/
    ├── insert_flower_semantic/
    ├── select_billiards_semantic/
    ├── select_book_semantic/
    ├── select_chemistry_tube_semantic/
    ├── select_drink_semantic/
    ├── select_fruit_semantic/
    ├── select_poker_semantic/
    └── select_toy_semantic/
├── Spatial/
    ├── add_condiment_spatial/
    ├── insert_bloom_flower/
    ├── select_billiards_spatial/
    ├── select_book_spatial/
    ├── select_chemistry_tube_spatial/
    ├── select_fruit_spatial/
    ├── select_poker_spatial/
    └── select_toy_spatial/
```
In each subtask, there are 100 episodes of data, such as:
```
vlm_evaluation_v1.0/
├── CommenSence/
    └── add_condiment_common_sense/
        ├── example0
            ├── env_config
            ├── input
            └── output     
     
        ├── ...
        └── example99
```

The `env_config` folder includes the episode_config for conveniently reproducing the evaluation environment.
The `input` folder includes the stacked four-view images and their segmentated visual prompted images as the visual input to VLMs, as well as the instruction to descripe the task.
The `output` folder includes the ground truth action sequence JSON file.

## Evaluate

To evaluate the dataset, please refer to our evaluation guidance in [repo](https://github.com/OpenMOSS/VLABench).

## Citation
If you find our work helps,please cite us:
```
@misc{zhang2024vlabench,
      title={VLABench: A Large-Scale Benchmark for Language-Conditioned Robotics Manipulation with Long-Horizon Reasoning Tasks}, 
      author={Shiduo Zhang and Zhe Xu and Peiju Liu and Xiaopeng Yu and Yuan Li and Qinghui Gao and Zhaoye Fei and Zhangyue Yin and Zuxuan Wu and Yu-Gang Jiang and Xipeng Qiu},
      year={2024},
      eprint={2412.18194},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2412.18194}, 
}
```