Add a 'normalized' card tag for datasets whose SO joints are left in
normalized units (uncalibrated -> APPROXIMATE) in run_migration.py, and
add tag_normalized.py to backfill the tag on already-migrated datasets.
Datasets with deleted episodes (e.g. '*_clean' variants) keep gaps in
their episode numbering across data, videos, and metadata. The stock
v2.1->v3.0 converter renumbers data/videos by sorted file order (0..N-1)
but reads original gapped indices from episodes.jsonl, so it raises
'Number of episodes is not the same'. Add reindex_episodes(): when every
source agrees on the same (non-contiguous) episode set, remap it to
0..N-1 everywhere (data files + episode_index/index columns, per-camera
videos, episodes.jsonl, episodes_stats.jsonl, info.json) so conversion
succeeds. Verified end-to-end on danaaubakirova/svla_so100_task4_v3_clean
(gaps {20,37,38,39} -> 0..49).
If the data files and any camera's video files (or two cameras) don't have the
same number of episodes, skip the dataset up front instead of letting the
converter raise 'All cams dont have same number of episodes' mid-run.
When the data files and video files agree on episode count N but the metadata
lists a different count, rewrite meta/episodes.jsonl, meta/episodes_stats.jsonl
and info.json to N before the v2.1->v3.0 converter runs (which otherwise raises
'Number of episodes is not the same'). Only the safe direction is handled:
trimming metadata that lists MORE episodes than exist. Non-contiguous data,
data/video disagreement, or metadata missing episodes are left untouched.
A robot_type of so100/so101 is treated as wrong when the joint dim isn't a
multiple of 6, or (when names are present) the first 6 joints don't match the
canonical SO set. Such datasets are migrated structurally to v3.0 with joints
left untouched and robot_type relabeled 'unknown', instead of being skipped or
degrees-converted on a false assumption.
Only migrate datasets usable right away as a clean 6-DOF joint stack. SO datasets
whose action/observation.state dim isn't a multiple of 6 (extra bbox/EE columns
appended) are now skipped as out-of-scope, matching the end-effector skip, rather
than being relabeled '_nonstandard' and migrated structurally.
Some datasets store task-space end-effector pose (names like ee_x/ee_roll or
x/y/z) instead of joint angles; the degrees mapping is meaningless there. Detect
via feature names and skip them entirely (no conversion, no upload) rather than
migrating a mislabeled arm.
download_subfolder gains a repo= arg so it can source from any monorepo; the new
extract_dataset.py scoped-downloads one sub-dataset and re-uploads it at the root
of a new standalone dataset repo.
- download_subfolder: fetch only the target sub-dataset subtree instead of
enumerating the whole community_dataset_v3 monorepo tree (fixes apparent hang)
- normalized SO gripper (RANGE_0_100) left in native 0..100 frame, matching
degrees_new datasets, instead of remapping to +/-45deg
- uncalibrated normalized datasets: skip identity value rewrite, keep normalized
units and flag them APPROXIMATE on the dataset card
- remove --allow-uncalibrated flag and its CANON_IS_CALIBRATED side effect
CarolinePascal