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metadata 
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

arXiv GitHub Dataset

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 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 

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 

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 

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.

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.

Citation

If you use this benchmark in your research, please cite:

@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.

Contact

For questions or issues, please open an issue on the GitHub repository or contact the EO-1 team.