Search is not available for this dataset
observation.state list | action list | timestamp float32 | frame_index int64 | episode_index int64 | index int64 | task_index int64 |
|---|---|---|---|---|---|---|
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End of preview. Expand in Data Studio
yam7h-ego
Retargeted egocentric human video, YAM bimanual.
Part of a 7.014 h co-fine-tuning mixture for pi0-FAST on the YAM bimanual arm:
| source | episodes | frames | hours |
|---|---|---|---|
angkul07/yam7h-teleop |
761 | 251,703 | 2.331 |
angkul07/yam7h-ego |
4,174 | 505,837 | 4.684 |
| total | 4,935 | 757,540 | 7.014 |
This dataset
| Episodes | 4,174 |
| Frames | 505,837 |
| Duration | 4.684 h |
| FPS | 30 |
| Codebase version | v2.1 |
| Source | angkul07/EgoDex-PickPlace-YAM-14dof-multiview |
| Views | observation.images.top, .left_wrist, .right_wrist (224x224) |
| State / action | 14-D (2x6 arm joints + 2 grippers) |
Selection
Retargeted egocentric human video (EgoDex -> YAM 14-DoF), filtered and subsampled.
Constraint: at least 30 episodes per object. Objects are not a metadata field —
they are parsed from the task sentence (Pick up a <object> from the <source> and place it ...). The parser is validated against the published category table and
reproduces all 269 distinct objects across 9 categories exactly; select_mixture.py
hard-fails if that check ever stops matching.
| stage | objects | episodes | frames | hours |
|---|---|---|---|---|
| source | 269 | 8,842 | 1,074,893 | 9.953 |
| min 30 eps/object | 111 | 6,548 | 794,288 | 7.355 |
| subsampled to target | 111 | 4,174 | 505,837 | 4.684 |
The threshold is applied to the source pool; the subsample is then proportional per object, so all 111 objects survive and the object mix is preserved. Note the smallest object ends at 20 episodes in the delivered data, since it keeps its proportional share.
471 task strings are retained verbatim in tasks.jsonl so task_index values stay
valid; not all are used after subsampling.
Known domain gaps vs teleop
Measured, not corrected — relevant if you train on this mixture:
- Gripper range collision. Teleop's right gripper is crisply bimodal (closed 0.333
/ open 0.984, 3.16 sigma). Retargeted ego is compressed (0.222 / 0.429, 1.83 sigma),
so ego's open lands on teleop's closed. Both use
0_closed_1_open; there is no polarity inversion, but the ranges overlap. - Synthesized wrist views are soft. Laplacian-variance sharpness, teleop
top/L/R = 1043.7 / 420.6 / 421.8 versus ego 93.4 / 62.2 / 38.1. This is missing
source information from edge-clamped warps, not compression — ego
topandright_wristhave near-identical bitrate yet differ 2.7x in sharpness. - No corrective signal.
action[t] == state[t+1]exactly for ego, versus a 0.683 correlation for teleop. Ego joint deltas are 2-4x narrower with clipping pile-ups at exactly +/-0.1 and +/-0.2.
A per-episode retargeting QA sidecar travels with the source dataset; the
active_ik_mean_cm <= 15 filter it recommends was not applied here (3,948 of the
4,174 selected episodes would pass it).
Episode renumbering
Episodes are renumbered 0..4173; original source indices are in
source_episodes.json (episodes list + index_map).
This is required, not cosmetic: lerobot's get_episodes_file_paths probes
range(meta.total_episodes), so a folder holding sparse original indices is
unopenable — it silently falls back to the Hub and 401s.
Verification
Built by vast_run/build_mixture.py and checked by vast_run/audit_mixture.py in
angkul07/openpi:
- 25 random episodes read back and compared column-by-column against the source
parquet —
observation.state,action,timestamp,frame_index,task_indexall byte-identical. Renumbering touches onlyepisode_indexand the globalindex. - 8 episodes x 3 cameras decoded with
ffprobe; every frame count matches its parquet row count. - Episode indices contiguous, global
indexcontiguous across the dataset, all parquet and video files present.
Reproducing the selection
vast_run/yam7h_manifest.json records the exact episode lists (seed 0).
python vast_run/select_mixture.py --dry-run # plan, downloads only meta/
python vast_run/build_mixture.py # fetch only selected episodes
python vast_run/audit_mixture.py # independent verification
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