Dataset Viewer
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code: FeaturesError
Exception: ArrowInvalid
Message: Schema at index 1 was different:
episode_index: int64
task_index: int64
start_frame: int64
end_frame: int64
num_frames: int64
duration: double
video_path: string
split: string
vs
episode_index: int64
stats: struct<action: struct<min: list<item: double>, max: list<item: double>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, observation.state: struct<min: list<item: double>, max: list<item: double>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, observation.images.main: struct<min: list<item: list<item: double>>, max: list<item: list<item: double>>, mean: list<item: list<item: double>>, std: list<item: list<item: double>>, count: list<item: int64>>, timestamp: struct<min: double, max: double, mean: double, std: double, count: list<item: int64>>, frame_index: struct<min: int64, max: int64, mean: double, std: double, count: list<item: int64>>, episode_index: struct<min: int64, max: int64, mean: double, std: double, count: list<item: int64>>, index: struct<min: int64, max: int64, mean: double, std: double, count: list<item: int64>>, task_index: struct<min: int64, max: int64, mean: double, std: double, count: list<item: int64>>>
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 228, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 3422, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2187, in _head
return next(iter(self.iter(batch_size=n)))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2391, in iter
for key, example in iterator:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1882, in __iter__
for key, pa_table in self._iter_arrow():
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1904, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 559, in _iter_arrow
yield new_key, pa.Table.from_batches(chunks_buffer)
File "pyarrow/table.pxi", line 4116, in pyarrow.lib.Table.from_batches
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Schema at index 1 was different:
episode_index: int64
task_index: int64
start_frame: int64
end_frame: int64
num_frames: int64
duration: double
video_path: string
split: string
vs
episode_index: int64
stats: struct<action: struct<min: list<item: double>, max: list<item: double>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, observation.state: struct<min: list<item: double>, max: list<item: double>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, observation.images.main: struct<min: list<item: list<item: double>>, max: list<item: list<item: double>>, mean: list<item: list<item: double>>, std: list<item: list<item: double>>, count: list<item: int64>>, timestamp: struct<min: double, max: double, mean: double, std: double, count: list<item: int64>>, frame_index: struct<min: int64, max: int64, mean: double, std: double, count: list<item: int64>>, episode_index: struct<min: int64, max: int64, mean: double, std: double, count: list<item: int64>>, index: struct<min: int64, max: int64, mean: double, std: double, count: list<item: int64>>, task_index: struct<min: int64, max: int64, mean: double, std: double, count: list<item: int64>>>Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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
[Add curator information]
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
[Add contribution information]
Contact
[Add contact information]
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