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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 datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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 |
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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