Datasets:

pranavsaroha
/

so100-synth-dataset-0726-9

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
episode_index: int64
tasks: list<item: null>
length: int64
vs
episode_index: int64
tasks: list<item: null>
length: 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: list<item: double>>>, max: list<item: list<item: list<item: double>>>, mean: list<item: list<item: list<item: double>>>, std: list<item: list<item: list<item: double>>>, count: list<item: int64>>, timestamp: struct<min: list<item: double>, max: list<item: double>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, frame_index: struct<min: list<item: int64>, max: list<item: int64>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, episode_index: struct<min: list<item: int64>, max: list<item: int64>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, index: struct<min: list<item: int64>, max: list<item: int64>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, task_index: struct<min: list<item: int64>, max: list<item: int64>, mean: list<item: double>, std: list<item: 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
              tasks: list<item: null>
              length: int64
              vs
              episode_index: int64
              tasks: list<item: null>
              length: 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: list<item: double>>>, max: list<item: list<item: list<item: double>>>, mean: list<item: list<item: list<item: double>>>, std: list<item: list<item: list<item: double>>>, count: list<item: int64>>, timestamp: struct<min: list<item: double>, max: list<item: double>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, frame_index: struct<min: list<item: int64>, max: list<item: int64>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, episode_index: struct<min: list<item: int64>, max: list<item: int64>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, index: struct<min: list<item: int64>, max: list<item: int64>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, task_index: struct<min: list<item: int64>, max: list<item: int64>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>>

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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?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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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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