Datasets:
action list | actions list | observations dict | timestamps float64 |
|---|---|---|---|
[-0.44294798374176025,-0.4000000059604645,1.5170135498046875,3.1415927410125732,0.0,0.0,0.0,0.0,0.0,(...TRUNCATED) | [-0.44294798374176025,-0.4000000059604645,1.5170135498046875,3.1415927410125732,0.0,0.0,0.0,0.0,0.0,(...TRUNCATED) | {"images":{"ego":[[[175,171,165],[172,168,162],[174,170,164],[174,170,164],[173,169,163],[173,169,16(...TRUNCATED) | 0 |
[-0.44294798374176025,-0.4000000059604645,1.5170135498046875,3.1415927410125732,0.0,0.0,0.0,0.0,0.0,(...TRUNCATED) | [-0.44294798374176025,-0.4000000059604645,1.5170135498046875,3.1415927410125732,0.0,0.0,0.0,0.0,0.0,(...TRUNCATED) | {"images":{"ego":[[[175,171,165],[172,168,162],[174,170,164],[174,170,164],[173,169,163],[173,169,16(...TRUNCATED) | 0.033333 |
[-0.44294798374176025,-0.4000000059604645,1.5170135498046875,3.1415927410125732,0.0,0.0,0.0,0.0,0.0,(...TRUNCATED) | [-0.44294798374176025,-0.4000000059604645,1.5170135498046875,3.1415927410125732,0.0,0.0,0.0,0.0,0.0,(...TRUNCATED) | {"images":{"ego":[[[175,171,165],[172,168,162],[174,170,164],[174,170,164],[173,169,163],[173,169,16(...TRUNCATED) | 0.066667 |
[-0.44294798374176025,-0.4000000059604645,1.5170135498046875,3.1415927410125732,0.0,0.0,0.0,0.0,0.0,(...TRUNCATED) | [-0.44294798374176025,-0.4000000059604645,1.5170135498046875,3.1415927410125732,0.0,0.0,0.0,0.0,0.0,(...TRUNCATED) | {"images":{"ego":[[[175,171,165],[172,168,162],[174,170,164],[174,170,164],[173,169,163],[173,169,16(...TRUNCATED) | 0.1 |
[-0.44294798374176025,-0.4000000059604645,1.5170135498046875,3.1415927410125732,0.0,0.0,0.0,0.0,0.0,(...TRUNCATED) | [-0.44294798374176025,-0.4000000059604645,1.5170135498046875,3.1415927410125732,0.0,0.0,0.0,0.0,0.0,(...TRUNCATED) | {"images":{"ego":[[[175,171,165],[172,168,162],[174,170,164],[174,170,164],[173,169,163],[173,169,16(...TRUNCATED) | 0.133333 |
[-0.44294798374176025,-0.4000000059604645,1.5170135498046875,3.1415927410125732,0.0,0.0,0.0,0.0,0.0,(...TRUNCATED) | [-0.44294798374176025,-0.4000000059604645,1.5170135498046875,3.1415927410125732,0.0,0.0,0.0,0.0,0.0,(...TRUNCATED) | {"images":{"ego":[[[175,171,165],[172,168,162],[174,170,164],[174,170,164],[173,169,163],[173,169,16(...TRUNCATED) | 0.166667 |
[-0.44294798374176025,-0.4000000059604645,1.5170135498046875,3.1415927410125732,0.0,0.0,0.0,0.0,0.0,(...TRUNCATED) | [-0.44294798374176025,-0.4000000059604645,1.5170135498046875,3.1415927410125732,0.0,0.0,0.0,0.0,0.0,(...TRUNCATED) | {"images":{"ego":[[[175,171,165],[172,168,162],[174,170,164],[174,170,164],[173,169,163],[173,169,16(...TRUNCATED) | 0.2 |
[-0.44294798374176025,-0.4000000059604645,1.5170135498046875,3.1415927410125732,0.0,0.0,0.0,0.0,0.0,(...TRUNCATED) | [-0.44294798374176025,-0.4000000059604645,1.5170135498046875,3.1415927410125732,0.0,0.0,0.0,0.0,0.0,(...TRUNCATED) | {"images":{"ego":[[[175,171,165],[172,168,162],[174,170,164],[174,170,164],[173,169,163],[173,169,16(...TRUNCATED) | 0.233333 |
[-0.44294798374176025,-0.4000000059604645,1.5170135498046875,3.1415927410125732,0.0,0.0,0.0,0.0,0.0,(...TRUNCATED) | [-0.44294798374176025,-0.4000000059604645,1.5170135498046875,3.1415927410125732,0.0,0.0,0.0,0.0,0.0,(...TRUNCATED) | {"images":{"ego":[[[175,171,165],[172,168,162],[174,170,164],[174,170,164],[173,169,163],[173,169,16(...TRUNCATED) | 0.266667 |
[-0.44294798374176025,-0.4000000059604645,1.5170135498046875,3.1415927410125732,0.0,0.0,0.0,0.0,0.0,(...TRUNCATED) | [-0.44294798374176025,-0.4000000059604645,1.5170135498046875,3.1415927410125732,0.0,0.0,0.0,0.0,0.0,(...TRUNCATED) | {"images":{"ego":[[[175,171,165],[172,168,162],[174,170,164],[174,170,164],[173,169,163],[173,169,16(...TRUNCATED) | 0.3 |
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
DexJoCo HDF5 Dataset for X-RLinf
This dataset contains converted DexJoCo demonstrations prepared for X-RLinf SimVLA training.
Dataset variants
normal/: normally converted DexJoCo demonstrationsrand_full/: randomized full-scene DexJoCo demonstrations
HDF5 format
Each episode contains:
/actions:[T, 22]/observations/qpos:[T, 23]/observations/images/front:[T, H, W, 3]/observations/images/wrist:[T, H, W, 3]
Tasks
- bimanual_assembly
- bimanual_hanoi
- bimanual_microwave_cook
- bimanual_photograph
- bimanual_unlock_ipad
- click_mouse
- fold_glasses
- hammer_nail
- pick_bucket
- pinch_tongs
- water_plant
Please refer to the original DexJoCo dataset and repository for license, attribution, and usage restrictions.
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