Datasets:

VGalaxies666
/

coffeeMakingDemo

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
episode_index: int64
stats: struct<action: struct<min: list<item: double>, max: list<item: double>, mean: list<item: double>, st (... 1454 chars omitted)
  child 0, action: struct<min: list<item: double>, max: list<item: double>, mean: list<item: double>, std: list<item: d (... 33 chars omitted)
      child 0, min: list<item: double>
          child 0, item: double
      child 1, max: list<item: double>
          child 0, item: double
      child 2, mean: list<item: double>
          child 0, item: double
      child 3, std: list<item: double>
          child 0, item: double
      child 4, count: list<item: int64>
          child 0, item: int64
  child 1, observation.state: struct<min: list<item: double>, max: list<item: double>, mean: list<item: double>, std: list<item: d (... 33 chars omitted)
      child 0, min: list<item: double>
          child 0, item: double
      child 1, max: list<item: double>
          child 0, item: double
      child 2, mean: list<item: double>
          child 0, item: double
      child 3, std: list<item: double>
          child 0, item: double
      child 4, count: list<item: int64>
          child 0, item: int64
  child 2, observation.images.cam_high: struct<min: list<item: list<item: list<item: double>>>, max: list<item: list<item: list<item: double (... 129 chars omitted)
      child 0, min: list<item: list<item: list<item: double>>>
          child 0, item: list<item: list<item: double>>
              child 0, item: list<item: double>
     
...
ax: list<item: int64>, mean: list<item: double>, std: list<item: dou (... 31 chars omitted)
      child 0, min: list<item: int64>
          child 0, item: int64
      child 1, max: list<item: int64>
          child 0, item: int64
      child 2, mean: list<item: double>
          child 0, item: double
      child 3, std: list<item: double>
          child 0, item: double
      child 4, count: list<item: int64>
          child 0, item: int64
  child 7, index: struct<min: list<item: int64>, max: list<item: int64>, mean: list<item: double>, std: list<item: dou (... 31 chars omitted)
      child 0, min: list<item: int64>
          child 0, item: int64
      child 1, max: list<item: int64>
          child 0, item: int64
      child 2, mean: list<item: double>
          child 0, item: double
      child 3, std: list<item: double>
          child 0, item: double
      child 4, count: list<item: int64>
          child 0, item: int64
  child 8, task_index: struct<min: list<item: int64>, max: list<item: int64>, mean: list<item: double>, std: list<item: dou (... 31 chars omitted)
      child 0, min: list<item: int64>
          child 0, item: int64
      child 1, max: list<item: int64>
          child 0, item: int64
      child 2, mean: list<item: double>
          child 0, item: double
      child 3, std: list<item: double>
          child 0, item: double
      child 4, count: list<item: int64>
          child 0, item: int64
length: int64
tasks: list<item: string>
  child 0, item: string
to
{'episode_index': Value('int64'), 'tasks': List(Value('string')), 'length': Value('int64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 478, 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 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, 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 343, 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 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              episode_index: int64
              stats: struct<action: struct<min: list<item: double>, max: list<item: double>, mean: list<item: double>, st (... 1454 chars omitted)
                child 0, action: struct<min: list<item: double>, max: list<item: double>, mean: list<item: double>, std: list<item: d (... 33 chars omitted)
                    child 0, min: list<item: double>
                        child 0, item: double
                    child 1, max: list<item: double>
                        child 0, item: double
                    child 2, mean: list<item: double>
                        child 0, item: double
                    child 3, std: list<item: double>
                        child 0, item: double
                    child 4, count: list<item: int64>
                        child 0, item: int64
                child 1, observation.state: struct<min: list<item: double>, max: list<item: double>, mean: list<item: double>, std: list<item: d (... 33 chars omitted)
                    child 0, min: list<item: double>
                        child 0, item: double
                    child 1, max: list<item: double>
                        child 0, item: double
                    child 2, mean: list<item: double>
                        child 0, item: double
                    child 3, std: list<item: double>
                        child 0, item: double
                    child 4, count: list<item: int64>
                        child 0, item: int64
                child 2, observation.images.cam_high: struct<min: list<item: list<item: list<item: double>>>, max: list<item: list<item: list<item: double (... 129 chars omitted)
                    child 0, min: list<item: list<item: list<item: double>>>
                        child 0, item: list<item: list<item: double>>
                            child 0, item: list<item: double>
                   
              ...
              ax: list<item: int64>, mean: list<item: double>, std: list<item: dou (... 31 chars omitted)
                    child 0, min: list<item: int64>
                        child 0, item: int64
                    child 1, max: list<item: int64>
                        child 0, item: int64
                    child 2, mean: list<item: double>
                        child 0, item: double
                    child 3, std: list<item: double>
                        child 0, item: double
                    child 4, count: list<item: int64>
                        child 0, item: int64
                child 7, index: struct<min: list<item: int64>, max: list<item: int64>, mean: list<item: double>, std: list<item: dou (... 31 chars omitted)
                    child 0, min: list<item: int64>
                        child 0, item: int64
                    child 1, max: list<item: int64>
                        child 0, item: int64
                    child 2, mean: list<item: double>
                        child 0, item: double
                    child 3, std: list<item: double>
                        child 0, item: double
                    child 4, count: list<item: int64>
                        child 0, item: int64
                child 8, task_index: struct<min: list<item: int64>, max: list<item: int64>, mean: list<item: double>, std: list<item: dou (... 31 chars omitted)
                    child 0, min: list<item: int64>
                        child 0, item: int64
                    child 1, max: list<item: int64>
                        child 0, item: int64
                    child 2, mean: list<item: double>
                        child 0, item: double
                    child 3, std: list<item: double>
                        child 0, item: double
                    child 4, count: list<item: int64>
                        child 0, item: int64
              length: int64
              tasks: list<item: string>
                child 0, item: string
              to
              {'episode_index': Value('int64'), 'tasks': List(Value('string')), 'length': Value('int64')}
              because column names don't match

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Coffee Making Demo (TsFile)

Apache TsFile version of allday-technology/coffeeMakingDemo.

Overview

A LeRobot (v2.1) teleoperation dataset of a coffee-making demonstration recorded on a Trossen AI stationary robot. Each episode is a single-arm (7-DoF) trajectory with synchronized proprioceptive state and action commands at 30 fps.

The dataset covers the following tasks:

pick up a coffee capsule
pick up an empty cup by the handle
Robot: trossen_ai_stationary, single arm, 7 joints (left_joint_0 … left_joint_6).
Episodes: 66 (episode_index 0–65).
Frames: 127,171 time-series rows.
Tasks: 3 task ids (task_index 0–2; see meta/tasks.jsonl for the instruction text).
Sampling rate: 30 fps (from meta/info.json).

Schema (TsFile structure)

All 66 episodes are stored in a single TsFile table; episode_index and task_index are TAG columns (the TsFile device dimension), so one episode is selected with WHERE episode_index=0.

Time (INT64, milliseconds) — round(timestamp * 1000); restarts at 0 for each episode, stepping by ~33 ms (30 fps).
episode_index (TAG) — source episode id, 0–65.
task_index (TAG) — source task id, 0–2; resolve to the instruction text via meta/tasks.jsonl.
frame_index (FIELD, INT64) — frame number within the episode.
sample_index (FIELD, INT64) — the source global index column, renamed.
observation_state_0 … observation_state_6 (FIELD, FLOAT) — robot proprioceptive joint state, flattened from the 7-element observation.state vector (left_joint_0..6).
action_0 … action_6 (FIELD, FLOAT) — joint action command, flattened from the 7-element action vector (left_joint_0..6).

The source timestamp column is dropped because it equals Time / 1000 seconds. No other columns or rows are dropped.

Videos

The two camera video streams of the original dataset (observation.images.cam_high, observation.images.cam_left_wrist) are NOT included in this repository. They are large and non–time-series; obtain them from the original dataset: https://huggingface.co/datasets/allday-technology/coffeeMakingDemo (the videos/ directory).

Usage

Read the .tsfile file with the Apache TsFile Java or Python SDK.

Source & license

Original dataset: https://huggingface.co/datasets/allday-technology/coffeeMakingDemo
Author / publisher: allday-technology
Created with: LeRobot
License: Apache-2.0 (as declared by the original dataset).

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Total file size:

2.94 MB