Datasets:

naavox
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plates_all

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train

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
run_id: string
kind: string
storage: string
file: string
telemetry: string
stream_start_ts: double
samples: int64
packets: int64
started_at: double
finished_at: double
notes: string
target_ranges: list<item: double>
  child 0, item: double
sweep_degrees: double
sweep_speed_dps: double
start_wrist_angle: double
robot_id: string
host: string
label: string
grasp_axis_wrist_angle: double
merged_from: string
frames: int64
image_format: string
width: int64
height: int64
finger_angles: list<item: int64>
  child 0, item: int64
wrist_steps: int64
base_wrist_angle: double
finger_angle: double
t: double
wrist_angle: double
finger_pressure: double
target_range_m: double
laser_rangefinder: double
to
{'t': Value('float64'), 'wrist_angle': Value('float64'), 'finger_angle': Value('float64'), 'finger_pressure': Value('float64'), 'laser_rangefinder': Value('float64'), 'target_range_m': Value('float64'), 'start_wrist_angle': Value('float64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, 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 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              run_id: string
              kind: string
              storage: string
              file: string
              telemetry: string
              stream_start_ts: double
              samples: int64
              packets: int64
              started_at: double
              finished_at: double
              notes: string
              target_ranges: list<item: double>
                child 0, item: double
              sweep_degrees: double
              sweep_speed_dps: double
              start_wrist_angle: double
              robot_id: string
              host: string
              label: string
              grasp_axis_wrist_angle: double
              merged_from: string
              frames: int64
              image_format: string
              width: int64
              height: int64
              finger_angles: list<item: int64>
                child 0, item: int64
              wrist_steps: int64
              base_wrist_angle: double
              finger_angle: double
              t: double
              wrist_angle: double
              finger_pressure: double
              target_range_m: double
              laser_rangefinder: double
              to
              {'t': Value('float64'), 'wrist_angle': Value('float64'), 'finger_angle': Value('float64'), 'finger_pressure': Value('float64'), 'laser_rangefinder': Value('float64'), 'target_range_m': Value('float64'), 'start_wrist_angle': Value('float64')}
              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 1694, 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 1880, 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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t float64	wrist_angle float64	finger_angle float64	finger_pressure float64	laser_rangefinder float64	target_range_m float64	start_wrist_angle float64
1,786,798,602.859213	13.095703	-89.77357	0.000237	0.135	0.12	13.095703
1,786,798,602.9099	13.095703	-89.77357	0.000238	0.135	0.12	13.095703
1,786,798,602.960846	13.095703	-89.77357	0.000239	0.135	0.12	13.095703
1,786,798,603.011375	13.359375	-89.77357	0.00024	0.138	0.12	13.095703
1,786,798,603.062032	14.677734	-89.77357	0.000238	0.134	0.12	13.095703
1,786,798,603.112343	15.205078	-89.77357	0.000238	0.134	0.12	13.095703
1,786,798,603.163086	15.732422	-89.77357	0.000238	0.136	0.12	13.095703
1,786,798,603.213502	16.259766	-89.77357	0.000238	0.136	0.12	13.095703
1,786,798,603.264459	17.314453	-89.77357	0.000238	0.134	0.12	13.095703
1,786,798,603.315549	17.929688	-89.77357	0.000239	0.134	0.12	13.095703
1,786,798,603.36675	18.369141	-89.77357	0.000239	0.132	0.12	13.095703
1,786,798,603.417644	18.896484	-89.77357	0.000239	0.132	0.12	13.095703
1,786,798,603.468725	20.039063	-89.77357	0.000238	0.139	0.12	13.095703
1,786,798,603.519368	20.566406	-89.77357	0.000238	0.139	0.12	13.095703
1,786,798,603.56976	21.09375	-89.77357	0.000239	0.133	0.12	13.095703
1,786,798,603.62018	21.621094	-89.77357	0.000239	0.133	0.12	13.095703
1,786,798,603.670913	22.675781	-89.77357	0.000238	0.135	0.12	13.095703
1,786,798,603.721105	23.203125	-89.77357	0.000239	0.135	0.12	13.095703
1,786,798,603.772046	23.818359	-89.77357	0.000238	0.134	0.12	13.095703
1,786,798,603.822444	24.433594	-89.77357	0.000239	0.134	0.12	13.095703
1,786,798,603.872937	25.048828	-89.77357	0.000238	0.136	0.12	13.095703
1,786,798,603.923564	26.103516	-89.77357	0.000237	0.136	0.12	13.095703
1,786,798,603.974168	26.71875	-89.77357	0.000237	0.136	0.12	13.095703
1,786,798,604.02485	27.246094	-89.77357	0.000238	0.135	0.12	13.095703
1,786,798,604.075427	27.773438	-89.77357	0.000236	0.135	0.12	13.095703
1,786,798,604.126576	28.916016	-89.77357	0.000237	0.134	0.12	13.095703
1,786,798,604.177611	29.53125	-89.77357	0.000237	0.134	0.12	13.095703
1,786,798,604.227769	30.058594	-89.77357	0.000236	0.137	0.12	13.095703
1,786,798,604.278203	30.673828	-89.77357	0.000237	0.137	0.12	13.095703
1,786,798,604.328565	31.640625	-89.77357	0.000236	0.134	0.12	13.095703
1,786,798,604.378878	32.167969	-89.77357	0.000236	0.134	0.12	13.095703
1,786,798,604.429556	32.695312	-89.77357	0.000236	0.135	0.12	13.095703
1,786,798,604.480367	33.310547	-89.77357	0.000235	0.135	0.12	13.095703
1,786,798,604.530749	33.75	-89.77357	0.000234	0.134	0.12	13.095703
1,786,798,604.581934	34.277344	-89.77357	0.000234	0.134	0.12	13.095703
1,786,798,604.632456	35.419922	-89.77357	0.000234	0.135	0.12	13.095703
1,786,798,604.682797	35.947266	-89.77357	0.000236	0.135	0.12	13.095703
1,786,798,604.733287	36.474609	-89.77357	0.000237	0.134	0.12	13.095703
1,786,798,604.78455	37.617187	-89.77357	0.000236	0.137	0.12	13.095703
1,786,798,604.8355	38.144531	-89.77357	0.000237	0.137	0.12	13.095703
1,786,798,604.885763	38.759766	-89.77357	0.000238	0.134	0.12	13.095703
1,786,798,604.935814	39.287109	-89.77357	0.000239	0.134	0.12	13.095703
1,786,798,604.985978	39.902344	-89.77357	0.000239	0.134	0.12	13.095703
1,786,798,605.036018	40.957031	-89.77357	0.000238	0.136	0.12	13.095703
1,786,798,605.086918	41.484375	-89.77357	0.000236	0.138	0.12	13.095703
1,786,798,605.137376	42.011719	-89.77357	0.000236	0.138	0.12	13.095703
1,786,798,605.187644	42.539063	-89.77357	0.000235	0.139	0.12	13.095703
1,786,798,605.238324	43.242187	-89.77357	0.000236	0.139	0.12	13.095703
1,786,798,605.28911	44.296875	-89.77357	0.000235	0.135	0.12	13.095703
1,786,798,605.339946	44.736328	-89.77357	0.000235	0.135	0.12	13.095703
1,786,798,605.390632	45.175781	-89.77357	0.000235	0.137	0.12	13.095703
1,786,798,605.441446	45.791016	-89.77357	0.000235	0.137	0.12	13.095703
1,786,798,605.491997	46.933594	-89.77357	0.000234	0.135	0.12	13.095703
1,786,798,605.542447	47.373047	-89.77357	0.000235	0.135	0.12	13.095703
1,786,798,605.593351	47.900391	-89.77357	0.000234	0.132	0.12	13.095703
1,786,798,605.643498	48.515625	-89.77357	0.000235	0.132	0.12	13.095703
1,786,798,605.694203	49.570312	-89.77357	0.000235	0.135	0.12	13.095703
1,786,798,605.744644	50.009766	-89.77357	0.000236	0.133	0.12	13.095703
1,786,798,605.795011	50.537109	-89.77357	0.000235	0.133	0.12	13.095703
1,786,798,605.845238	51.152344	-89.77357	0.000234	0.133	0.12	13.095703
1,786,798,605.896229	52.294922	-89.77357	0.000232	0.137	0.12	13.095703
1,786,798,605.947082	52.734375	-89.77357	0.000232	0.136	0.12	13.095703
1,786,798,605.997775	53.261719	-89.77357	0.000234	0.136	0.12	13.095703
1,786,798,606.048092	53.789062	-89.77357	0.000235	0.134	0.12	13.095703
1,786,798,606.098683	54.931641	-89.77357	0.000235	0.134	0.12	13.095703
1,786,798,606.149673	55.458984	-89.77357	0.000235	0.135	0.12	13.095703
1,786,798,606.199978	55.986328	-89.77357	0.000234	0.135	0.12	13.095703
1,786,798,606.250907	56.513672	-89.77357	0.000235	0.138	0.12	13.095703
1,786,798,606.301378	57.65625	-89.77357	0.000235	0.137	0.12	13.095703
1,786,798,606.351649	58.183594	-89.77357	0.000234	0.137	0.12	13.095703
1,786,798,606.402667	58.710938	-89.77357	0.000235	0.137	0.12	13.095703
1,786,798,606.453529	59.238281	-89.77357	0.000235	0.138	0.12	13.095703
1,786,798,606.504412	59.853516	-89.77357	0.000235	0.138	0.12	13.095703
1,786,798,606.554653	60.908203	-89.77357	0.000237	0.133	0.12	13.095703
1,786,798,606.605334	61.523437	-89.77357	0.000237	0.134	0.12	13.095703
1,786,798,606.655846	62.138672	-89.77357	0.000237	0.134	0.12	13.095703
1,786,798,606.706309	62.753906	-89.77357	0.000238	0.135	0.12	13.095703
1,786,798,606.756738	63.28125	-89.77357	0.000239	0.135	0.12	13.095703
1,786,798,606.807786	64.248047	-89.77357	0.000238	0.134	0.12	13.095703
1,786,798,606.858452	64.863281	-89.77357	0.000237	0.134	0.12	13.095703
1,786,798,606.908827	65.390625	-89.77357	0.000238	0.134	0.12	13.095703
1,786,798,606.9593	65.917969	-89.77357	0.000236	0.134	0.12	13.095703
1,786,798,607.009794	67.060547	-89.77357	0.000235	0.135	0.12	13.095703
1,786,798,607.060822	67.587891	-89.77357	0.000237	0.135	0.12	13.095703
1,786,798,607.111165	68.027344	-89.77357	0.000236	0.139	0.12	13.095703
1,786,798,607.161907	68.642578	-89.77357	0.000236	0.139	0.12	13.095703
1,786,798,607.213002	69.697266	-89.77357	0.000237	0.135	0.12	13.095703
1,786,798,607.26364	70.224609	-89.77357	0.000238	0.135	0.12	13.095703
1,786,798,607.314617	70.751953	-89.77357	0.000237	0.138	0.12	13.095703
1,786,798,607.365629	71.279297	-89.77357	0.000236	0.138	0.12	13.095703
1,786,798,607.416322	72.421875	-89.77357	0.000235	0.135	0.12	13.095703
1,786,798,607.466407	72.861328	-89.77357	0.000237	0.134	0.12	13.095703
1,786,798,607.517251	73.564453	-89.77357	0.000235	0.134	0.12	13.095703
1,786,798,607.568352	74.091797	-89.77357	0.000236	0.134	0.12	13.095703
1,786,798,607.618699	74.619141	-89.77357	0.000235	0.137	0.12	13.095703
1,786,798,607.668948	75.761719	-89.77357	0.000235	0.133	0.12	13.095703
1,786,798,607.72	76.289063	-89.77357	0.000235	0.133	0.12	13.095703
1,786,798,607.770748	76.289063	-89.77357	0.000235	0.133	0.12	13.095703
1,786,798,607.82161	76.289063	-89.77357	0.000235	0.133	0.12	13.095703
1,786,798,607.872347	76.289063	-89.77357	0.000235	0.133	0.12	13.095703

End of preview.

plates_all

Raw plate captures for the stringman visual servoing dataset: what the gripper camera saw, stored exactly as it came off the robot. Nothing here is matted, keyed or composited.

kind	runs	plates
fingerplates	3	4536
floorplates	15	23142
objectplates	39	60297

Each run is a parquet file of encoded frames, or a .ts video plus a .jsonl telemetry track, described by one line of manifest.jsonl. Read it with nf_robot.ml.visual_servoing.plates, and pull it into a local collection with:

python -m nf_robot.ml.visual_servoing.merge_plates --into plates --from naavox/plates_all

Mattes and synthetic frames are derived from these by finger_matte.py, object_matte.py and synth_frames.py, whose thresholds are still being tuned - so they are rebuilt from this, never stored alongside it.

Downloads last month: 79

Total file size:

1.55 GB