Datasets:
metadata
datasets:
- StaticEmbodiedBench
language:
- en
tags:
- embodied-AI
- vlm
- vision-language
- multiple-choice
license: mit
pretty_name: StaticEmbodiedBench
task_categories:
- visual-question-answering
π Dataset Description
StaticEmbodiedBench is a dataset for evaluating vision-language models on embodied intelligence tasks, as featured in the OpenCompass leaderboard.
It covers three key capabilities:
- Macro Planning: Decomposing a complex task into a sequence of simpler subtasks.
- Micro Perception: Performing concrete simple tasks such as spatial understanding and fine-grained perception.
- Stage-wise Reasoning: Deciding the next action based on the agentβs current state and perceptual inputs.
Each sample is also labeled with a visual perspective:
- First-Person View: The visual sensor is integrated with the agent, e.g., mounted on the end-effector.
- Third-Person View: The visual sensor is separate from the agent, e.g., top-down or observer view.
This release includes 200 open-source samples from the full dataset, provided for public research and benchmarking purposes.
π‘ Usage
This dataset is fully supported by VLMEvalKit.
π§ Evaluate with VLMEvalKit
Registered dataset names:
StaticEmbodiedBench
β for standard evaluationStaticEmbodiedBench_circular
β for circular evaluation (multi-round)
To run evaluation in VLMEvalKit:
python run.py --data StaticEmbodiedBench --model <your_model_name> --verbose
For circular evaluation, simply use:
python run.py --data StaticEmbodiedBench_circular --model <your_model_name> --verbose
π Citation
If you use this dataset in your research, please cite it as follows:
@misc{staticembodiedbench,
title = {StaticEmbodiedBench},
author = {Jiahao Xiao, Shengyu Guo, Chunyi Li, Bowen Yan and Jianbo Zhang},
year = {2025},
url = {https://huggingface.co/datasets/xiaojiahao/StaticEmbodiedBench}
}