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int64
[ 10.546875, 174.990234375, 171.650390625, 44.47265625, -7.734375, 2.109375, 100.26881408691406 ]
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2.166667
65
1
454
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2.2
66
1
455
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2.233333
67
1
456
0
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2.266667
68
1
457
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2.3
69
1
458
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2.333333
70
1
459
0
[ 17.2265625, 131.572265625, 135.615234375, 41.044921875, -6.767578125, -1.494140625, 100.26881408691406 ]
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2.366667
71
1
460
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2.4
72
1
461
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2.433333
73
1
462
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2.466667
74
1
463
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[ 17.2265625, 128.583984375, 133.681640625, 41.8359375, -6.328125, -1.7578125, 96.83698272705078 ]
2.5
75
1
464
0
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2.533333
76
1
465
0
[ 17.138671875, 124.541015625, 129.287109375, 42.5390625, -5.625, -2.021484375, 100.26881408691406 ]
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2.566667
77
1
466
0
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2.6
78
1
467
0
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2.633333
79
1
468
0
[ 17.05078125, 120.498046875, 124.892578125, 44.736328125, -5.09765625, -2.28515625, 100.26881408691406 ]
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2.666667
80
1
469
0
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2.7
81
1
470
0
[ 16.962890625, 118.125, 122.080078125, 46.142578125, -4.658203125, -2.373046875, 100.26881408691406 ]
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2.733333
82
1
471
0
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2.766667
83
1
472
0
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2.8
84
1
473
0
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2.833333
85
1
474
0
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2.866667
86
1
475
0
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2.9
87
1
476
0
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2.933333
88
1
477
0
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2.966667
89
1
478
0
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3
90
1
479
0
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3.033333
91
1
480
0
[ 17.138671875, 111.533203125, 116.015625, 50.185546875, -2.900390625, -2.109375, 100.26881408691406 ]
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3.066667
92
1
481
0
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3.1
93
1
482
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3.133333
94
1
483
0
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3.166667
95
1
484
0
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3.2
96
1
485
0
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[ 16.875, 109.599609375, 115.3125, 51.767578125, -3.1640625, -1.58203125, 96.83698272705078 ]
3.233333
97
1
486
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[ 16.875, 109.423828125, 115.048828125, 52.119140625, -2.900390625, -1.494140625, 96.83698272705078 ]
3.266667
98
1
487
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3.3
99
1
488
0
End of preview. Expand in Data Studio
YAML Metadata Warning: The task_ids "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, 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 1g0rrr/merged_valid

Dataset Structure

This dataset follows the LeRobot v2.1 format with the following structure:

  • meta/info.json: Dataset metadata and configuration
  • meta/episodes.jsonl: Episode information including tasks and lengths
  • meta/tasks.jsonl: Task descriptions and indices
  • meta/episodes_stats.jsonl: Per-episode statistics
  • data/chunk_*/episode_*.parquet: Episode data files
  • videos/chunk_*/video_key_*/episode_*.mp4: Video files (if present)

Usage

This dataset can be loaded using the LeRobot dataset loader:

from lerobot.common.datasets.lerobot_dataset import LeRobotDataset

dataset = LeRobotDataset("1g0rrr/merged_valid")
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