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Datasets:
Raybeezy
/
Video_Subset

Tasks:
Video Classification
Robotics
Modalities:
Tabular
Video
Formats:
parquet
Languages:
English
Size:
100K - 1M
Tags:
egocentric
exocentric
first-person
third-person
robotics
lerobot
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
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metadata 
license: mit
task_categories:
  - video-classification
  - robotics
language:
  - en
tags:
  - egocentric
  - exocentric
  - first-person
  - third-person
  - robotics
  - lerobot
  - smartphone
size_categories:
  - 1K<n<10K

Video Subset Dataset

A HuggingFace LeRobot-compatible dataset containing 763 episodes of egocentric and exocentric videos from indoor environments with corresponding AR pose data.

Dataset Summary

  • Total Episodes: 763 (Egocentric: 243, Exocentric: 520)
  • Total Frames: 321,178
  • Frame Rate: 30.00 FPS
  • Codebase Version: v2.0

Tasks

The dataset contains two distinct viewpoint tasks:

  • Task 0 (Egocentric): First-person view videos - 243 episodes (31.8%)
  • Task 1 (Exocentric): Third-person view videos - 520 episodes (68.2%)

Each episode's data includes a task_index field to distinguish between egocentric and exocentric videos.

Environments

Videos were collected from 4 different indoor rooms:

Room Episodes Egocentric Exocentric
Bedroom 39 14 25
Sandwich 54 41 13
Laundry 436 156 280
Bathroom 234 32 202

Dataset Structure 

{
  "episode_index": int,
  "frame_index": int,
  "timestamp": float,
  "observation.state": List[float],  # 7D state (placeholder)
  "action": List[float],             # 7D pose: [quat_x,y,z,w, pos_x,y,z]
  "next.reward": float,
  "next.done": bool,
  "next.success": bool,
  "task_index": int,                 # 0=egocentric, 1=exocentric
  "index": int
}

Action Space

The action field contains 7-dimensional AR pose vectors:

  • Quaternion (4 values): Camera rotation as [x, y, z, w]
  • Position (3 values): Camera translation as [x, y, z]

This data comes from smartphone AR tracking during video recording.

Usage

Load with HuggingFace Datasets 

from datasets import load_dataset

# Load full dataset
dataset = load_dataset("YOUR_USERNAME/seesawvideos-lerobot")

# Access frames
print(dataset['train'][0])

Filter by Task (Egocentric vs Exocentric) 

# Filter egocentric only (task_index = 0)
egocentric = dataset['train'].filter(lambda x: x['task_index'] == 0)

# Filter exocentric only (task_index = 1)
exocentric = dataset['train'].filter(lambda x: x['task_index'] == 1)

Using Episode Metadata

The meta/episodes.csv file contains detailed metadata for filtering:

import pandas as pd

# Load episode metadata
episodes = pd.read_csv("meta/episodes.csv")

# Filter by task and room
ego_laundry = episodes[
    (episodes['task_label'] == 'egocentric') &
    (episodes['room'] == 'Laundry')
]

print(f"Found {len(ego_laundry)} egocentric Laundry episodes")

Using Provided Utilities 

from utils import SeesawDatasetFilter

dataset = SeesawDatasetFilter(".")

# Get egocentric episodes
ego_episodes = dataset.get_episodes(task="egocentric")

# Filter by room and duration
bathroom_short = dataset.get_episodes(
    room="Bathroom",
    max_duration=10.0
)

# Get video paths
ego_videos = dataset.get_video_paths(task="egocentric")

Files Included

  • data/chunk-000/*.parquet - Frame-level data for all episodes
  • videos/chunk-000/observation.image/*.mp4 - Video files
  • meta/info.json - Dataset metadata and configuration
  • meta/episodes.csv - Per-episode metadata with labels and room info
  • meta/splits.json - Pre-computed train/val/test splits (70/15/15)
  • utils.py - Helper functions for filtering and loading
  • example_usage.py - Complete usage examples
  • FILTERING_GUIDE.md - Quick reference for filtering by task

Statistics

Duration by Task

  • Egocentric: 12.5 ± 11.2 seconds (range: 1.7 - 82.2s)
  • Exocentric: 14.7 ± 22.7 seconds (range: 1.5 - 387.6s)

Frames by Task

  • Egocentric: 375 ± 335 frames (range: 51 - 2,467)
  • Exocentric: 443 ± 680 frames (range: 46 - 11,629)

Citation

If you use this dataset, please cite:

@dataset{seesawvideos_lerobot_2026,
  title={SeesawVideos LeRobot Dataset},
  author={Your Name},
  year={2026},
  publisher={HuggingFace},
  howpublished={\url{https://huggingface.co/datasets/YOUR_USERNAME/seesawvideos-lerobot}}
}

License

MIT License

Additional Resources