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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    TypeError
Message:      Couldn't cast array of type
list<item: uint8>
to
List(Value('uint8'), length=26180)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1779, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 91, in _generate_tables
                  yield Key(file_idx, batch_idx), cast_table_to_features(pa_table, self.info.features)
                                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2228, in cast_table_to_features
                  arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
                            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1804, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2011, in cast_array_to_feature
                  _c(array.field(name) if name in array_fields else null_array, subfeature)
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1806, in wrapper
                  return func(array, *args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2011, in cast_array_to_feature
                  _c(array.field(name) if name in array_fields else null_array, subfeature)
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1806, in wrapper
                  return func(array, *args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2101, in cast_array_to_feature
                  raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
              TypeError: Couldn't cast array of type
              list<item: uint8>
              to
              List(Value('uint8'), length=26180)
              
              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 1348, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 882, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 943, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1832, 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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observations
dict
raw_input
dict
timestamps
float64
{ "images": { "wrist_left_rgb": [ 255, 216, 255, 224, 0, 16, 74, 70, 73, 70, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 255, 219, 0, 67, 0, 5, 3, 4, 4, ...
{ "clamp_left": 7.664654089599095, "clamp_right": 87.54047435463723, "fastumi_pose_left": [ -0.791802352774395, 0.16454359645281327, -0.5046290248784466, -0.7558737405414102, -0.06802375921811316, -0.05347583978213579, 0.6489745689172619 ], "fastumi_pose_right": [ -0.3744080050...
1,780,311,016.995677
{"images":{"wrist_left_rgb":[255,216,255,224,0,16,74,70,73,70,0,1,1,0,0,1,0,1,0,0,255,219,0,67,0,5,3(...TRUNCATED)
{"clamp_left":7.515737442166474,"clamp_right":87.54956808582004,"fastumi_pose_left":[-0.787524780449(...TRUNCATED)
1,780,311,017.029043
{"images":{"wrist_left_rgb":[255,216,255,224,0,16,74,70,73,70,0,1,1,0,0,1,0,1,0,0,255,219,0,67,0,5,3(...TRUNCATED)
{"clamp_left":20.872873195818666,"clamp_right":87.54956808582004,"fastumi_pose_left":[-0.78518802265(...TRUNCATED)
1,780,311,017.062376
{"images":{"wrist_left_rgb":[255,216,255,224,0,16,74,70,73,70,0,1,1,0,0,1,0,1,0,0,255,219,0,67,0,5,3(...TRUNCATED)
{"clamp_left":60.82576817613872,"clamp_right":87.54956808582004,"fastumi_pose_left":[-0.782252465307(...TRUNCATED)
1,780,311,017.09568
{"images":{"wrist_left_rgb":[255,216,255,224,0,16,74,70,73,70,0,1,1,0,0,1,0,1,0,0,255,219,0,67,0,5,3(...TRUNCATED)
{"clamp_left":86.0732472182847,"clamp_right":87.54956808582004,"fastumi_pose_left":[-0.7813649285157(...TRUNCATED)
1,780,311,017.128959
{"images":{"wrist_left_rgb":[255,216,255,224,0,16,74,70,73,70,0,1,1,0,0,1,0,1,0,0,255,219,0,67,0,5,3(...TRUNCATED)
{"clamp_left":83.13407330003281,"clamp_right":87.54047435463723,"fastumi_pose_left":[-0.781285483879(...TRUNCATED)
1,780,311,017.162334
{"images":{"wrist_left_rgb":[255,216,255,224,0,16,74,70,73,70,0,1,1,0,0,1,0,1,0,0,255,219,0,67,0,5,3(...TRUNCATED)
{"clamp_left":84.91026147513712,"clamp_right":87.54047435463723,"fastumi_pose_left":[-0.781176761411(...TRUNCATED)
1,780,311,017.19564
{"images":{"wrist_left_rgb":[255,216,255,224,0,16,74,70,73,70,0,1,1,0,0,1,0,1,0,0,255,219,0,67,0,5,3(...TRUNCATED)
{"clamp_left":86.50675492058879,"clamp_right":87.54047435463723,"fastumi_pose_left":[-0.781034853079(...TRUNCATED)
1,780,311,017.229302
{"images":{"wrist_left_rgb":[255,216,255,224,0,16,74,70,73,70,0,1,1,0,0,1,0,1,0,0,255,219,0,67,0,5,3(...TRUNCATED)
{"clamp_left":86.23028745366973,"clamp_right":87.54956808582004,"fastumi_pose_left":[-0.780488642143(...TRUNCATED)
1,780,311,017.262413
{"images":{"wrist_left_rgb":[255,216,255,224,0,16,74,70,73,70,0,1,1,0,0,1,0,1,0,0,255,219,0,67,0,5,3(...TRUNCATED)
{"clamp_left":86.23951666914971,"clamp_right":87.54956808582004,"fastumi_pose_left":[-0.779324249112(...TRUNCATED)
1,780,311,017.29752
End of preview.

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

FastUMI Bottle Pick-and-Place Dataset (Raw)

Overview

45 episodes of bottle pick-and-place teleoperation data collected with FastUMI Pro on R1Pro (26') humanoid robot.

Task: Pick up a bottle from the table, place it at another location, return to start.

Six categories covering local/mobile/back modes and normal/fast speeds:

Category Mode Speed Episodes Total Frames Total Duration Avg Duration
local_standard Local (base fixed) Normal 10 4473 149.1s 14.9s
local_fast Local (base fixed) Fast 5 1011 33.7s 6.7s
mobile_standard Mobile (cross-table) Normal 10 4339 144.6s 14.5s
mobile_fast Mobile (cross-table) Fast 5 1278 42.6s 8.5s
back_standard Back (turn & place behind) Normal 10 4703 156.8s 15.7s
back_fast Back (turn & place behind) Fast 5 1538 51.3s 10.3s
  • Active hand: Left hand
  • Recording frequency: 30 Hz
  • Total: 45 episodes, 17342 frames, 578s, ~868 MB

Directory Structure

bottle_raw_dataset/
  local_standard/     # 10 episodes, base fixed, normal speed
    episode_01.hdf5
    ...
    episode_10.hdf5
  local_fast/          # 5 episodes, base fixed, fast speed
    episode_01.hdf5
    ...
    episode_05.hdf5
  mobile_standard/     # 10 episodes, cross-table, normal speed
    episode_01.hdf5
    ...
    episode_10.hdf5
  mobile_fast/          # 5 episodes, cross-table, fast speed
    episode_01.hdf5
    ...
    episode_05.hdf5
  back_standard/        # 10 episodes, turn & place behind, normal speed
    episode_01.hdf5
    ...
    episode_10.hdf5
  back_fast/             # 5 episodes, turn & place behind, fast speed
    episode_01.hdf5
    ...
    episode_05.hdf5
  README.md

Data Format

Each episode_XX.hdf5 contains:

attributes:
  task            str       e.g. "standard_1"
  batch           str       e.g. "standard"
  category        str       e.g. "local_standard"
  num_steps       int       number of frames
  record_freq_hz  int       30
  success         bool      True
  image_size      int       320
  image_compression str     "jpeg"

datasets:
  timestamps                          (N,)            float64
  raw_input/
    fastumi_pose_left                 (N, 7)          float64   [px,py,pz,qx,qy,qz,qw]
    fastumi_pose_right                (N, 7)          float64
    clamp_left                        (N,)            float64   0~90
    clamp_right                       (N,)            float64   0~90
  observations/images/
    wrist_left_rgb                    (N, max_len)    uint8     JPEG bytes (320x320)
    wrist_left_rgb_len                (N,)            int32     valid length per frame
    wrist_right_rgb                   (N, max_len)    uint8     JPEG bytes (320x320)
    wrist_right_rgb_len               (N,)            int32     valid length per frame

Decoding JPEG images

import h5py, numpy as np, cv2

f = h5py.File("local_standard/episode_01.hdf5", "r")
jpeg_data = f["observations/images/wrist_right_rgb"]
jpeg_lens = f["observations/images/wrist_right_rgb_len"]

frame_idx = 0
buf = jpeg_data[frame_idx, :jpeg_lens[frame_idx]]
img = cv2.imdecode(np.frombuffer(buf, np.uint8), cv2.IMREAD_COLOR)  # (320, 320, 3) BGR

Collection Setup

  • Robot: R1Pro 26' (Galaxea)
  • Device: FastUMI Pro (dual handheld SLAM + gripper sensor + RGB camera)
  • Right serial: SN250801DR48FP25002313
  • Left serial: SN250801DR48FP25002692
  • Trigger: Double-click gripper to start/stop recording
  • SLAM coordinate: X=left, Y=down, Z=forward (camera direction)
  • Collector script: fastumi_collector.py

Category Details

local_standard

Standard pick-and-place at a single table. Robot base fixed. Right hand reaches forward, grabs bottle, moves to another position on the same table, places bottle, returns.

local_fast

Same task as local_standard but performed at higher speed. Tests fast motion tracking capability.

mobile_standard

Cross-table pick-and-place. Involves walking/turning with the bottle. Right hand grabs bottle from one table, carries it to another table, places it, and returns.

mobile_fast

Same task as mobile_standard but performed at higher speed.

back_standard

Turn-and-place-behind task. Left hand grabs bottle from front table, turns body ~180°, places bottle on the table behind, then turns back and returns to start position.

back_fast

Same task as back_standard but performed at higher speed.

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