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README.md

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+---
+license: apache-2.0
+task_categories:
+  - visual-question-answering
+  - image-to-text
+language:
+  - en
+tags:
+  - robotics
+  - embodied-ai
+  - multimodal
+  - benchmark
+  - vision-language
+  - EO-1
+pretty_name: EO-Bench
+size_categories:
+  - n<1K
+---
+
+# EO-Bench: Embodied Reasoning Benchmark for Vision-Language Models
+
+<p align="center">
+  <a href="https://arxiv.org/abs/2508.21112"><img src="https://img.shields.io/badge/arXiv-2508.21112-b31b1b.svg" alt="arXiv"></a>
+  <a href="https://github.com/SHAILAB-IPEC/EO1"><img src="https://img.shields.io/badge/GitHub-EO--1-blue.svg" alt="GitHub"></a>
+  <a href="https://huggingface.co/datasets/IPEC-COMMUNITY/EO-Data1.5M"><img src="https://img.shields.io/badge/🤗-EO--Data1.5M-yellow.svg" alt="Dataset"></a>
+</p>
+
+## Overview
+
+**EO-Bench** is a comprehensive benchmark designed to evaluate the **embodied reasoning** capabilities of vision-language models (VLMs) in robotics scenarios. This benchmark is part of the [EO-1](https://github.com/SHAILAB-IPEC/EO1) project, which develops unified embodied foundation models for general robot control.
+
+The benchmark assesses model performance across **12 distinct embodied reasoning categories**, covering trajectory reasoning, visual grounding, action reasoning, and more. All tasks are presented in a multiple-choice format to enable standardized evaluation.
+
+## Dataset Description
+
+### Statistics
+
+| Metric | Value |
+|--------|-------|
+| Total Samples | 600 |
+| Question Types | 12 |
+| Total Images | 668 |
+| Answer Format | Multiple Choice (A/B/C/D) |
+
+### Question Type Distribution
+
+| Question Type | Count |
+|---------------|-------|
+| Trajectory Reasoning | 134 |
+| Visual Grounding | 128 |
+| Process Verification | 113 |
+| Multiview Pointing | 68 |
+| Relation Reasoning | 40 |
+| Robot Interaction | 37 |
+| Object State | 34 |
+| Episode Caption | 17 |
+| Action Reasoning | 13 |
+| Task Planning | 10 |
+| Direct Influence | 4 |
+| Counterfactual | 2 |
+
+### Data Fields
+
+Each sample contains the following fields:
+
+- **`id`** (int): Unique identifier for each sample
+- **`question`** (str): The question text with multiple-choice options
+- **`question_type`** (str): Category of the question (one of 12 types)
+- **`answer`** (str): Correct answer letter (A, B, C, or D)
+- **`num_images`** (int): Number of images associated with the question
+- **`image_paths`** (list[str]): Relative paths to the associated images
+
+### Question Type Descriptions
+
+| Type | Description |
+|------|-------------|
+| **Trajectory Reasoning** | Predict the optimal path for robot end-effector movement |
+| **Visual Grounding** | Locate specific objects or regions in the scene |
+| **Process Verification** | Verify the correctness of a robotic action sequence |
+| **Multiview Pointing** | Identify corresponding points across multiple camera views |
+| **Relation Reasoning** | Understand spatial relationships between objects |
+| **Robot Interaction** | Predict outcomes of robot-environment interactions |
+| **Object State** | Recognize and reason about object states |
+| **Episode Caption** | Describe robotic manipulation episodes |
+| **Action Reasoning** | Reason about the effects of robot actions |
+| **Task Planning** | Plan sequences of actions to achieve goals |
+| **Direct Influence** | Understand direct causal effects in manipulation |
+| **Counterfactual** | Reason about hypothetical alternative scenarios |
+
+## Usage
+
+### Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("IPEC-COMMUNITY/EO-Bench")
+
+# Access samples
+for sample in dataset['train']:
+    print(f"ID: {sample['id']}")
+    print(f"Question: {sample['question']}")
+    print(f"Type: {sample['question_type']}")
+    print(f"Answer: {sample['answer']}")
+    print(f"Images: {sample['image_paths']}")
+    break
+```
+
+### Loading Images
+
+```python
+from PIL import Image
+from datasets import load_dataset
+import os
+
+dataset = load_dataset("IPEC-COMMUNITY/EO-Bench")
+
+# Get the first sample
+sample = dataset['train'][0]
+
+# Load associated images
+for img_path in sample['image_paths']:
+    # Images are stored in the 'images' folder
+    image = Image.open(hf_hub_download(
+        repo_id="IPEC-COMMUNITY/EO-Bench",
+        filename=img_path,
+        repo_type="dataset"
+    ))
+    image.show()
+```
+
+### Evaluation Example
+
+```python
+from datasets import load_dataset
+
+def evaluate_model(model, dataset):
+    correct = 0
+    total = 0
+    
+    for sample in dataset['train']:
+        # Load images and prepare input
+        images = [load_image(p) for p in sample['image_paths']]
+        question = sample['question']
+        
+        # Get model prediction
+        prediction = model.predict(images, question)
+        
+        # Check if correct
+        if prediction == sample['answer']:
+            correct += 1
+        total += 1
+    
+    accuracy = correct / total * 100
+    return accuracy
+```
+
+## Related Resources
+
+### EO-1 Model
+
+EO-1 is a unified embodied foundation model that processes interleaved vision-text-action inputs using a single decoder-only transformer architecture. The model achieves state-of-the-art performance on multimodal embodied reasoning tasks.
+
+- **Paper**: [EmbodiedOneVision: Interleaved Vision-Text-Action Pretraining for General Robot Control](https://arxiv.org/abs/2508.21112)
+- **GitHub**: [SHAILAB-IPEC/EO1](https://github.com/SHAILAB-IPEC/EO1)
+- **Models**: [IPEC-COMMUNITY/EO-1-3B](https://huggingface.co/IPEC-COMMUNITY/EO-1-3B)
+
+### Training Data
+
+EO-1 is trained on **EO-Data1.5M**, a comprehensive multimodal embodied reasoning dataset with over 1.5 million high-quality interleaved samples.
+
+- **Dataset**: [IPEC-COMMUNITY/EO-Data1.5M](https://huggingface.co/datasets/IPEC-COMMUNITY/EO-Data1.5M)
+
+## Citation
+
+If you use this benchmark in your research, please cite:
+
+```bibtex
+@article{eo1,
+  title={EmbodiedOneVision: Interleaved Vision-Text-Action Pretraining for General Robot Control},
+  author={EO-1 Team},
+  journal={arXiv preprint arXiv:2508.21112},
+  year={2025}
+}
+```
+
+## License
+
+This dataset is released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).
+
+## Contact
+
+For questions or issues, please open an issue on the [GitHub repository](https://github.com/SHAILAB-IPEC/EO1) or contact the EO-1 team.