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Add simulation One-Shot semantic subtask annotations
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{
"schema_version": 1,
"annotation_revision": 2,
"annotation_status": "user_confirmed_release",
"source_dataset": {
"repo_id": "wzlllong/value-model-benchmark-training-data",
"subtree": "data/simulation/one-shot",
"data_revision": "43ae4845fe0461c66aa59a0ee441185d99fb05f8",
"source_subtree_tree_id": "837e271aac36ea3eea72599ab5bca86db64bb025"
},
"dataset": {
"codebase_version": "v3.0",
"fps": 25,
"total_episodes": 15,
"total_frames": 8080,
"total_videos": 45
},
"paper": {
"url": "https://arxiv.org/abs/2610.26663",
"revision": "2610.26663v1",
"section": "C.2 Task Descriptions and Subtask Decomposition"
},
"segment_count": 58,
"paper_subtask_count": 58,
"annotation_target": "Active semantic subtask for each training-demonstration frame; not success, reward, or task-completion probability.",
"release_scope": "Supplemental annotations for simulation One-Shot training demonstrations, separate from benchmark test-set labels described in paper Section 3.4.",
"coverage_policy": "Every frame receives one printed paper subtask. Initial waiting belongs to the first subtask; terminal tail and retraction belong to the final subtask through episode end.",
"interval_convention": "[start_frame, end_frame); inclusive_end_frame = end_frame - 1",
"join_keys": [
"episode_index",
"frame_index"
],
"global_index_check": "global_index equals the LeRobot data index column",
"timestamp_convention": "Nominal timestamp_seconds = frame_index / 25; associate frames by integer keys, not exact float timestamp equality.",
"boundary_band": "Typically 13 frames (0.52 seconds); retained segment-specific widths are suggested adjustment bands, not measured confidence bounds or guarantees of frame-perfect precision.",
"boundary_uncertain_policy": "For each segment, mark its first t frames after an internal start and last t frames before an internal end; episode outer boundaries excluded. Keep all frame labels regardless of mask.",
"video_loading": {
"recommended_lerobot": "0.4.2",
"recommended_pyav": "15.1.0",
"default_tolerance_seconds": 0.0001,
"semantic_band_changes_loader_tolerance": false
},
"source_training_payload_modified": false,
"printed_label_policy": "Use the paper's printed S definitions, including Press By Number's S1 first button, S2 blue, S3 second button, S4 blue again; preserve the accepted order without rewriting the paper instruction.",
"task_aliases": {
"sweep_objects": "sweep_blocks"
},
"episodes": [
{
"episode_index": 0,
"task_index": 0,
"task_key": "arrange_largest_number",
"source_task_key": "arrange_largest_number",
"length": 579,
"dataset_from_index": 0,
"dataset_to_index": 579
},
{
"episode_index": 1,
"task_index": 1,
"task_key": "fill_pen_holder",
"source_task_key": "fill_pen_holder",
"length": 934,
"dataset_from_index": 579,
"dataset_to_index": 1513
},
{
"episode_index": 2,
"task_index": 2,
"task_key": "fold_clothes",
"source_task_key": "fold_clothes",
"length": 301,
"dataset_from_index": 1513,
"dataset_to_index": 1814
},
{
"episode_index": 3,
"task_index": 3,
"task_key": "hang_mugs",
"source_task_key": "hang_mugs",
"length": 513,
"dataset_from_index": 1814,
"dataset_to_index": 2327
},
{
"episode_index": 4,
"task_index": 4,
"task_key": "insert_tubes",
"source_task_key": "insert_tubes",
"length": 320,
"dataset_from_index": 2327,
"dataset_to_index": 2647
},
{
"episode_index": 5,
"task_index": 5,
"task_key": "make_toast",
"source_task_key": "make_toast",
"length": 584,
"dataset_from_index": 2647,
"dataset_to_index": 3231
},
{
"episode_index": 6,
"task_index": 6,
"task_key": "organize_table",
"source_task_key": "organize_table",
"length": 839,
"dataset_from_index": 3231,
"dataset_to_index": 4070
},
{
"episode_index": 7,
"task_index": 7,
"task_key": "pack_objects_into_box",
"source_task_key": "pack_objects_into_box",
"length": 414,
"dataset_from_index": 4070,
"dataset_to_index": 4484
},
{
"episode_index": 8,
"task_index": 8,
"task_key": "play_stacking_toy",
"source_task_key": "play_stacking_toy",
"length": 888,
"dataset_from_index": 4484,
"dataset_to_index": 5372
},
{
"episode_index": 9,
"task_index": 9,
"task_key": "press_by_number",
"source_task_key": "press_by_number",
"length": 378,
"dataset_from_index": 5372,
"dataset_to_index": 5750
},
{
"episode_index": 10,
"task_index": 10,
"task_key": "put_bottles_into_dustbin",
"source_task_key": "put_bottles_into_dustbin",
"length": 363,
"dataset_from_index": 5750,
"dataset_to_index": 6113
},
{
"episode_index": 11,
"task_index": 11,
"task_key": "stack_blocks",
"source_task_key": "stack_blocks",
"length": 268,
"dataset_from_index": 6113,
"dataset_to_index": 6381
},
{
"episode_index": 12,
"task_index": 12,
"task_key": "store_laptop_and_headphones",
"source_task_key": "store_laptop_and_headphones",
"length": 551,
"dataset_from_index": 6381,
"dataset_to_index": 6932
},
{
"episode_index": 13,
"task_index": 13,
"task_key": "swap_blocks",
"source_task_key": "swap_blocks",
"length": 445,
"dataset_from_index": 6932,
"dataset_to_index": 7377
},
{
"episode_index": 14,
"task_index": 14,
"task_key": "sweep_blocks",
"source_task_key": "sweep_objects",
"length": 703,
"dataset_from_index": 7377,
"dataset_to_index": 8080
}
]
}