The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
keyframes: list<item: int64>
child 0, item: int64
meta: list<item: struct<box: list<item: int64>, frame: int64>>
child 0, item: struct<box: list<item: int64>, frame: int64>
child 0, box: list<item: int64>
child 0, item: int64
child 1, frame: int64
total_frames: int64
to
{'keyframes': List(Value('int64')), 'total_frames': Value('int64')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 544, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 361, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
keyframes: list<item: int64>
child 0, item: int64
meta: list<item: struct<box: list<item: int64>, frame: int64>>
child 0, item: struct<box: list<item: int64>, frame: int64>
child 0, box: list<item: int64>
child 0, item: int64
child 1, frame: int64
total_frames: int64
to
{'keyframes': List(Value('int64')), 'total_frames': Value('int64')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
RoboDojo progress annotations
Frame-level labels over the official RoboDojo episodes, for the six tasks that
require remembering something seen earlier in the episode. They carry no images,
states, actions or episodes; the observations and actions a policy trains on
come from RoboDojo-Benchmark/Robodojo.
Published so that the training recipe behind our RoboDojo entry can be run by anyone: without these labels the keyframe-memory stage has nothing to learn from.
What is in it
600 files, <task>/trajectory_<n>/progress.json, covering all 100 episodes of
each of the six tasks. Plus index.json, which maps each task directory to the
official task instruction and states the schema.
| field | meaning |
|---|---|
keyframes |
the frames at which the task made visible progress that has to be remembered |
meta[].frame / meta[].box |
[x1, y1, x2, y2] in raw-video pixels, the object that progress was about |
meta[].window |
[first, last] — the frames at which recording that keyframe still counts as correct |
total_frames |
the episode's length, so a file can be checked against the episode it describes |
meta[].window exists because a keyframe is a moment, not a frame: several
consecutive frames show the same progress, so a selector that answers one frame
early is not wrong. Training scores the answer against the whole interval
rather than against a single index.
Per-task shape
| task | episodes | keyframes each | boxes each | acceptance intervals each |
|---|---|---|---|---|
cover_blocks |
100 | 4 | — | — |
imitate_sorting_sequence |
100 | 5 | 5 | — |
match_and_pick_from_conveyor |
100 | 1 | — | 1 |
press_by_number |
100 | 5–21 | — | — |
swap_T |
100 | 1 | — | — |
swap_blocks |
100 | 7 | — | 1 |
Where a task has no acceptance interval, the selection answer is scored against the single annotated candidate.
swap_T and match_and_pick_from_conveyor carry one keyframe, at frame 0: what
has to be remembered is the initial position and orientation of the two blocks,
and the first object on the conveyor. Both are visible at the start.
Checking them against the official episodes
An annotation bound to the wrong episode would train a policy to recall the wrong frames. Each file therefore records its episode's length; the loader binds by task instruction and stops if a length disagrees.
How they were produced
For each task we decided by hand what counts as a keyframe — the moments at which the task makes the progress a policy has to remember, such as a block being covered or a button press being committed. A script then located the frames that satisfy the rule in each episode, and every file was checked by a person and corrected where the script had put a frame in the wrong place.
Using them
hf download x-square-robot/robodojo-memory-annotations --repo-type dataset \
--local-dir <bench_root>/data/robodojo_memory_annotations
The training code that reads them is in
wall-x
(wall_x/data/backends/lerobot/robodojo/); see the WallX policy README in XPolicyLab
for the two training stages.
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