Dataset Viewer
	| id
				 stringclasses 9
				values | images
				 dict | conversations
				 listlengths 2 2 | 
|---|---|---|
| 
	10332_final_part1 | {"observation_images_chest":["iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAIAAAC6s0uzAAEAAElEQVR4nOz9WbAsSXYYiJ(...TRUNCATED) | [{"from":"human","value":"Here are the observations of cameras on a robot in the given time sequence(...TRUNCATED) | 
| 
	11699_middle_part2 | {"observation_images_chest":["iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAIAAAC6s0uzAAEAAElEQVR4nOz9WbBl2XUYiK(...TRUNCATED) | [{"from":"human","value":"Here are the observations of cameras on a robot in the given time sequence(...TRUNCATED) | 
| 
	26655_final_part2 | {"observation_images_chest":["iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAIAAAC6s0uzAAEAAElEQVR4nOz9WYwlWXYgiJ(...TRUNCATED) | [{"from":"human","value":"Here are the observations of cameras on a robot in the given time sequence(...TRUNCATED) | 
| 
	10332_final_part2 | {"observation_images_chest":["iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAIAAAC6s0uzAAEAAElEQVR4nOz9WbAsSXYYiJ(...TRUNCATED) | [{"from":"human","value":"Here are the observations of cameras on a robot in the given time sequence(...TRUNCATED) | 
| 
	21032_final_part0 | {"observation_images_chest":["iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAIAAAC6s0uzAAEAAElEQVR4nOz96bNkyXUfCP(...TRUNCATED) | [{"from":"human","value":"Here are the observations of cameras on a robot in the given time sequence(...TRUNCATED) | 
| 
	10332_final_part0 | {"observation_images_chest":["iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAIAAAC6s0uzAAEAAElEQVR4nOz9WbAsSXYYiJ(...TRUNCATED) | [{"from":"human","value":"Here are the observations of cameras on a robot in the given time sequence(...TRUNCATED) | 
| 
	21032_final_part2 | {"observation_images_chest":["iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAIAAAC6s0uzAAEAAElEQVR4nOz96bNkyXUfCP(...TRUNCATED) | [{"from":"human","value":"Here are the observations of cameras on a robot in the given time sequence(...TRUNCATED) | 
| 
	11699_middle_part0 | {"observation_images_chest":["iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAIAAAC6s0uzAAEAAElEQVR4nOz9WbBl2XUYiK(...TRUNCATED) | [{"from":"human","value":"Here are the observations of cameras on a robot in the given time sequence(...TRUNCATED) | 
| 
	11699_middle_part1 | {"observation_images_chest":["iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAIAAAC6s0uzAAEAAElEQVR4nOz9WbBl2XUYiK(...TRUNCATED) | [{"from":"human","value":"Here are the observations of cameras on a robot in the given time sequence(...TRUNCATED) | 
Condition Checking Dataset
This dataset contains condition checking conversations for robotics applications, with embedded base64 images from multiple camera viewpoints.
Dataset Structure
Data Fields
- id: Unique identifier for each sample
- images: Dictionary containing base64-encoded images from multiple camera viewpoints
- conversations: List of conversation turns (human question + assistant answer)
Camera Viewpoints
The dataset includes images from 5 camera viewpoints:
- observation_images_chest
- observation_images_left_eye
- observation_images_left_wrist
- observation_images_right_eye
- observation_images_right_wrist
Each sample contains approximately 30 images total (6 per camera).
Sample Structure
{
  "id": "frame_index_position_part",
  "images": {
    "camera_key": ["base64_image_1", "base64_image_2", ...],
    ...
  },
  "conversations": [
    {
      "from": "human",
      "value": "Here are the observations... condition: (object is grasped) ..."
    },
    {
      "from": "gpt", 
      "value": "True"
    }
  ]
}
Usage
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("jeffshen4011/condition-checking-dataset")
# Access a sample
sample = dataset["train"][0]
print(f"Question: {sample['conversations'][0]['value'][:100]}...")
print(f"Answer: {sample['conversations'][1]['value']}")
print(f"Number of camera views: {len(sample['images'])}")
Dataset Statistics
- Training samples: 9
- Camera viewpoints: 5
- Images per sample: ~30
- Image format: Base64-encoded PNG
- Task type: Binary classification (True/False)
Applications
This dataset is designed for:
- Training vision-language models for robotics condition checking
- Multi-modal reasoning tasks
- Robot state verification
- Visual question answering in manipulation contexts
Citation
If you use this dataset, please cite:
@dataset{condition_checking_dataset,
  title={Condition Checking Dataset for Robotics},
  author={Research Team},
  year={2025},
  url={https://huggingface.co/datasets/jeffshen4011/condition-checking-dataset}
}
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Task
		
			: Visual condition checking (True/False questions about robot states)
Modality
		
			: Multi-modal (text + images)
Domain
		
			: Robotics manipulation tasks
Format
		
			: Conversational format suitable for VLM training
Size of downloaded dataset files:
		
			11.5 MB
Size of the auto-converted Parquet files:
		
			11.5 MB
Number of rows:
		
			12