Datasets:
episode_id int32 0 49 | episode_file stringclasses 50
values | frame_id int32 0 328 | left_wrist imagewidth (px) 640 640 | right_wrist imagewidth (px) 640 640 | top imagewidth (px) 640 640 | qpos listlengths 14 14 | qvel listlengths 14 14 | action listlengths 14 14 |
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End of preview. Expand in Data Studio
YAML Metadata Warning:The task_categories "computer-vision" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
YAML Metadata Warning:The task_categories "imitation-learning" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
Robot Multi-View HDF5 Dataset
Dataset description
This dataset was converted from episode-level HDF5 files into a frame-level Hugging Face dataset.
- Total episode files: 100
- Split: train only
- Each row corresponds to one frame
- Each row contains:
- left wrist camera image
- right wrist camera image
- top camera image
- qpos
- qvel
- action
Data structure
Columns:
episode_id: integer episode index after sorting the input HDF5 filesepisode_file: original HDF5 file nameframe_id: frame index inside the episodeleft_wrist: left wrist RGB imageright_wrist: right wrist RGB imagetop: top RGB imageqpos: original 14D robot state vectorqvel: original 14D robot velocity/state vectoraction: original 14D action vector
Notes
This dataset preserves the original 14D definitions of qpos, qvel, and action without splitting them into subfields.
Users can perform their own train/test split after downloading the dataset.
Loading example
from datasets import load_dataset
ds = load_dataset("akino222333/dobot_Xtrainer_teddybear_pickup_passover_and_place_dataset")
print(ds["train"][0].keys())
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