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Datasets:
THUgewu
/
roboturk_lerobot

Tasks:
Robotics
Modalities:
Time-series
Size:
100K<n<1M
ArXiv:
Tags:
tsfile
timeseries
time-series
lerobot
robotics
roboturk
License:
Dataset card Data Studio Files
xet
 Community
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The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
episode_index: int64
stats: struct<observation.images.front_rgb: struct<min: list<item: list<item: list<item: double>>>, max: li (... 1189 chars omitted)
  child 0, observation.images.front_rgb: 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>
                  child 0, item: double
      child 1, max: list<item: list<item: list<item: double>>>
          child 0, item: list<item: list<item: double>>
              child 0, item: list<item: double>
                  child 0, item: double
      child 2, mean: list<item: list<item: list<item: double>>>
          child 0, item: list<item: list<item: double>>
              child 0, item: list<item: double>
                  child 0, item: double
      child 3, std: list<item: list<item: list<item: double>>>
          child 0, item: list<item: list<item: double>>
              child 0, item: 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 
...
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 6, 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 7, 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 "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                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<observation.images.front_rgb: struct<min: list<item: list<item: list<item: double>>>, max: li (... 1189 chars omitted)
                child 0, observation.images.front_rgb: 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>
                                child 0, item: double
                    child 1, max: list<item: list<item: list<item: double>>>
                        child 0, item: list<item: list<item: double>>
                            child 0, item: list<item: double>
                                child 0, item: double
                    child 2, mean: list<item: list<item: list<item: double>>>
                        child 0, item: list<item: list<item: double>>
                            child 0, item: list<item: double>
                                child 0, item: double
                    child 3, std: list<item: list<item: list<item: double>>>
                        child 0, item: list<item: list<item: double>>
                            child 0, item: 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 
              ...
              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 6, 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 7, 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
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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episode_index
int64
tasks
list
length
int64
0
[ "object search" ]
163
1
[ "object search" ]
275
2
[ "layout laundry" ]
102
3
[ "object search" ]
117
4
[ "object search" ]
151
5
[ "object search" ]
113
6
[ "layout laundry" ]
83
7
[ "create tower" ]
118
8
[ "create tower" ]
180
9
[ "object search" ]
70
10
[ "layout laundry" ]
129
11
[ "layout laundry" ]
100
12
[ "create tower" ]
92
13
[ "layout laundry" ]
80
14
[ "create tower" ]
128
15
[ "object search" ]
98
16
[ "layout laundry" ]
39
17
[ "layout laundry" ]
3
18
[ "layout laundry" ]
18
19
[ "create tower" ]
142
20
[ "layout laundry" ]
45
21
[ "layout laundry" ]
29
22
[ "layout laundry" ]
51
23
[ "create tower" ]
159
24
[ "layout laundry" ]
54
25
[ "layout laundry" ]
52
26
[ "layout laundry" ]
27
27
[ "create tower" ]
140
28
[ "layout laundry" ]
19
29
[ "create tower" ]
129
30
[ "layout laundry" ]
55
31
[ "create tower" ]
169
32
[ "create tower" ]
172
33
[ "layout laundry" ]
16
34
[ "object search" ]
174
35
[ "layout laundry" ]
16
36
[ "layout laundry" ]
21
37
[ "create tower" ]
141
38
[ "object search" ]
73
39
[ "layout laundry" ]
69
40
[ "layout laundry" ]
89
41
[ "layout laundry" ]
45
42
[ "create tower" ]
139
43
[ "layout laundry" ]
47
44
[ "layout laundry" ]
128
45
[ "layout laundry" ]
134
46
[ "layout laundry" ]
29
47
[ "object search" ]
142
48
[ "object search" ]
182
49
[ "layout laundry" ]
145
50
[ "object search" ]
48
51
[ "create tower" ]
126
52
[ "object search" ]
74
53
[ "object search" ]
69
54
[ "layout laundry" ]
22
55
[ "object search" ]
208
56
[ "layout laundry" ]
38
57
[ "layout laundry" ]
38
58
[ "create tower" ]
119
59
[ "layout laundry" ]
73
60
[ "create tower" ]
179
61
[ "object search" ]
128
62
[ "object search" ]
152
63
[ "object search" ]
121
64
[ "create tower" ]
133
65
[ "create tower" ]
152
66
[ "create tower" ]
92
67
[ "object search" ]
88
68
[ "object search" ]
72
69
[ "create tower" ]
164
70
[ "layout laundry" ]
48
71
[ "create tower" ]
154
72
[ "create tower" ]
144
73
[ "layout laundry" ]
49
74
[ "object search" ]
149
75
[ "create tower" ]
172
76
[ "object search" ]
126
77
[ "layout laundry" ]
64
78
[ "layout laundry" ]
42
79
[ "create tower" ]
155
80
[ "create tower" ]
86
81
[ "object search" ]
135
82
[ "layout laundry" ]
47
83
[ "object search" ]
151
84
[ "layout laundry" ]
51
85
[ "object search" ]
100
86
[ "object search" ]
95
87
[ "layout laundry" ]
100
88
[ "layout laundry" ]
16
89
[ "object search" ]
164
90
[ "object search" ]
89
91
[ "layout laundry" ]
71
92
[ "layout laundry" ]
32
93
[ "create tower" ]
57
94
[ "layout laundry" ]
148
95
[ "layout laundry" ]
171
96
[ "layout laundry" ]
4
97
[ "layout laundry" ]
63
98
[ "create tower" ]
166
99
[ "object search" ]
68
End of preview.

RoboTurk (Sawyer) (TsFile)

Apache TsFile version of IPEC-COMMUNITY/roboturk_lerobot.

Overview

RoboTurk is a large-scale robot manipulation dataset of human teleoperation demonstrations collected on a Sawyer robot arm, distributed here as part of the Open X-Embodiment collection in the LeRobot v2.1 format. Each record is one control frame within a demonstration episode, holding the robot's 8-D proprioceptive state and the 7-D end-effector action.

  • Scale: 1,796 episodes, 168,423 frames total (≈ 94 frames per episode on average).
  • Sampling rate: 10 fps.
  • Robot: Sawyer arm.
  • Tasks: 3 — object search, layout laundry, create tower (see task_index).

Schema (TsFile structure)

  • Time (INT64, milliseconds) — round(timestamp * 1000), restarting at 0 for each episode.
  • episode_index (TAG) — source episode id (0–1795).
  • task_index (TAG) — source task id (0 = object search, 1 = layout laundry, 2 = create tower).
  • frame_index (FIELD, INT64) — frame position within the episode.
  • sample_index (FIELD, INT64) — the source global index column, renamed.
  • observation_state_0 .. observation_state_7 (FIELD, FLOAT) — the 8-D state (motor_0..7), flattened.
  • action_0 .. action_6 (FIELD, FLOAT) — the 7-D action (x, y, z, roll, pitch, yaw, gripper), flattened.

All 1,796 episodes share a single .tsfile; episode_index and task_index are the device (TAG) dimensions. Query one episode with WHERE episode_index=0, or a whole task with WHERE task_index=1.

Vector columns are flattened keeping the source name (observation.state → observation_state_0..7, action → action_0..6). The source timestamp column is dropped because it equals Time / 1000 seconds; frame_index is kept.

Usage

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

Source & license

  • Original dataset: https://huggingface.co/datasets/IPEC-COMMUNITY/roboturk_lerobot
  • Author / publisher: IPEC-COMMUNITY (Hugging Face); original RoboTurk dataset.
  • Paper: RoboTurk — A Crowdsourcing Platform for Robotic Skill Learning through Imitation (https://arxiv.org/abs/1811.02790)
  • Part of the Open X-Embodiment collection.
  • Camera video stream (observation.images.front_rgb, 448×448 RGB @ 10 fps) is not included in this repository; see the original dataset's videos/ directory for it.
  • License: apache-2.0.
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