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pranavsaroha
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so100-synth-dataset-0726-3

Tasks:
Robotics
Modalities:
Tabular
Video
Formats:
parquet
Languages:
English
Size:
< 1K
Tags:
robotics
imitation-learning
teleoperation
video
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pandas
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observation.state
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End of preview. Expand in Data Studio
YAML Metadata Warning: The task_ids "robotics-manipulation" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-modeling, dialogue-generation, conversational, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, text2text-generation, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering, pose-estimation

Dataset Card for so100 Teleop Dataset

Dataset Summary

This dataset contains teleoperation data for so100 robot manipulation tasks. The dataset includes:

  • Videos: RGB video recordings of robot manipulation
  • Joint States: Per-frame joint angle data
  • Actions: Robot joint actions derived from state differences
  • Metadata: Episode and frame indexing information

Supported Tasks and Leaderboards

This dataset is designed for robot imitation learning and manipulation tasks.

Languages

The dataset contains English language metadata.

Dataset Structure

Data Instances

Each episode contains:

  • Video frames at 24.0 FPS
  • Joint state data for 6 joints
  • Action data for robot control
  • Timestamp information
  • Episode and frame indexing

Data Fields

  • observation.state: Joint angle data (float32, shape: [6])
  • action: Robot joint actions (float32, shape: [6])
  • observation.images.main: RGB video data (video, shape: [704, 1280, 3])
  • frame_index: Frame index within episode (int64)
  • episode_index: Episode identifier (int64)
  • index: Global frame index (int64)
  • task_index: Task identifier (int64)
  • timestamp: Time from episode start (float32)
  • next.done: Episode termination flag (bool)

Data Splits

  • Train: 348 frames across 1 episode

Dataset Creation

Source Data

Initial Data Collection and Normalization

The dataset was created from teleoperation recordings of so100 robot manipulation tasks.

Who are the source language producers?

[Add information about data collection process]

Annotations

Annotation process

[Add information about annotation process if applicable]

Who are the annotators?

[Add information about annotators if applicable]

Personal and Sensitive Information

[Add information about personal/sensitive data handling]

Additional Information

Dataset Curators

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Licensing Information

This dataset is licensed under the MIT License.

Citation Information 

@dataset{so100_teleop_dataset,
  title = {so100 Teleop Dataset},
  author = {[Add author information]},
  year = {2024},
  url = {[Add dataset URL]}
}

Contributions

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Contact

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Downloads last month
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Repository:
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Paper:
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Point of Contact:
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Size of downloaded dataset files:
32.1 kB
Size of the auto-converted Parquet files:
32.1 kB
Number of rows:
348