Elvinky/pi05-so101-fold-clothes-v11-human1to1-fp32-step8357
Robotics • 4B • Updated • 16
Search is not available for this dataset
action listlengths 12 12 | observation.state listlengths 12 12 | intervention bool 2
classes | timestamp float32 0 1.22k | frame_index int64 0 36.6k | episode_index int64 0 21 | index int64 0 422k | task_index int64 0 0 |
|---|---|---|---|---|---|---|---|
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End of preview. Expand in Data Studio
SO101 Clothes Folding DAgger
22 retained saved episodes from two dual-SO101 setups (4090B and 4090C), merged from 7 datasets in native LeRobot v3 format. These are mixed policy rollouts with human takeover. The complete retained episodes are included; human-control segments have not been extracted into separate episodes.
Task: Fold the clothes on the left side and stack them on the right.
The original task text is retained. Sampled images from every retained episode were visually reviewed to confirm the clothes-folding task, rather than relying only on dataset titles. This confirms the task identity, not successful completion of every rollout.
| Property | Value |
|---|---|
| Collection dates | 2026-09-09 evening to 2026-09-10 shortly after midnight, Asia/Shanghai |
| Episodes | 22 |
| Frames | 421,767 |
| FPS | 30 |
| Saved duration | 03:54:18.9 (14,058.900 seconds) |
| Human intervention frames | 106,957 |
| Contiguous human intervention segments | 429 |
| Human intervention duration | 00:59:25.2 (3,565.233 seconds) |
| Robot type | bi_so_follower |
| Action / state dimensions | 12 / 12 |
| Cameras | left_wrist, environment_1, right_wrist |
| Collection method | Episodic DAgger with policy rollout and human takeover |
The boolean intervention field is true for saved human-control frames and false for policy-control frames. Durations are saved frame counts divided by 30 FPS, excluding reset, waiting, and unsaved recordings. Human intervention time is a subset of the saved duration.
| Source | Datasets | Episodes | Saved duration (s) | Human intervention (s) |
|---|---|---|---|---|
| 4090B | 4 | 11 | 6,762.867 | 1,765.200 |
| 4090C | 3 | 11 | 7,296.033 | 1,800.033 |
| Total | 7 | 22 | 14,058.900 | 3,565.233 |
Sources and processing
Per-episode source mappings, original review labels, durations, and human-intervention frame intervals are in meta/source_episodes.json. Each interval uses an inclusive start and exclusive end relative to the source episode. Per-dataset totals are in meta/source_datasets.json. Merged episode indices run from 0 to 21; the original B1–B4/C1–C3 labels remain available in the source mapping.
Two episodes were explicitly excluded before export: B1E1 (source batch ending 20260909_202314, original episode index 1) and B4E1 (source batch ending 20260909_232100, original episode index 1). They are not included in the counts above.
Merged using LeRobot's native aggregation utility, following the same structure as the stationery DAgger dataset. Video files were copied from the curated sources without further re-encoding. Episode and global frame indices were updated, and task indices were mapped to the merged task table. All other saved data columns were compared against their source values and matched exactly. The native aggregation utility changes Arrow list storage from fixed-size lists to lists while retaining the declared feature dimensions and the exact numeric values. Source-to-output video SHA-256 hashes matched. Episode lengths, timestamps, and source-to-output video intervals were checked.
Known source issue
B1E0 (merged episode 0) has 16,478 saved data rows but only 16,477 frames in the original left-wrist video, a one-frame difference at 30 FPS. The other two cameras have 16,478 frames. The existing left-wrist discrepancy is preserved; no synthetic frame was inserted, no row was removed, and no frame was borrowed from the excluded episode. The exact location of the original missing capture is unknown. The default strict LeRobot decoder raises a timestamp-tolerance error on the final left-wrist sample of this episode. With tolerance_s=0.034, the PyAV backend successfully reads it by selecting the closest existing frame. This does not reconstruct the missing capture. Consumers requiring exact one-video-frame-per-row alignment should handle this episode explicitly. Before this export, B1 videos were split using LeRobot's native video tools when removing B1E1; this involved re-encoding the retained B1 clips. B4E0 and the other curated source videos were copied as stored.
Episode durations
The labels below refer to the original review labels in meta/source_episodes.json.
Load with LeRobot
from lerobot.datasets.lerobot_dataset import LeRobotDataset
dataset = LeRobotDataset(
"Elvinky/so101-fold-clothes-dagger-20260909",
video_backend="pyav",
tolerance_s=0.034, # Allows the documented one-frame gap in B1E0.
)
Use a LeRobot version supporting the native v3 dataset format. The intervention flag is retained as an additional per-frame feature.
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Models trained or fine-tuned on Elvinky/so101-fold-clothes-dagger-20260909